Abstract The rapid growth of decentralized AI applications has created a fundamental tension between computational integrity, model confidentiality, latency, and economic efficiency. Existing verification approaches, including zero-knowledge machine learning (zkML), optimistic machine learning (opML), and trusted execution environments (TEEs), provide strong guarantees along some dimensions but fail to simultaneously satisfy the practical requirements of large-scale AI inference systems deployed on blockchain infrastructure. This paper introduces AZR, a risk-adaptive verification architecture for decentralized AI inference on blockchain rollups. AZR dynamically selects among TEE attestation, optimistic fraud proofs, and zero-knowledge verification according to a query-specific risk function that captures economic value, adversarial exposure, and dispute likelihood. By allocating stronger verification mechanisms only to high-risk workloads, AZR balances security with operational efficiency while preserving computational integrity, model confidentiality, and input privacy. We formalize the verifier selection problem as a constrained optimization framework and analyze its security and economic properties under rational adversaries. Experimental evaluation across representative workloads, including ResNet-50, BERT-Base, and LLaMA-7B, demonstrates that AZR achieves substantial cost reductions relative to uniform zkML deployment while maintaining strong security guarantees. Under a representative workload distribution, AZR reduces verification costs by up to 61% compared with pure zkML systems, while enabling low-latency responses for the majority of inference requests. These results suggest that adaptive verification architectures provide a practical pathway toward scalable and trustworthy decentralized AI systems, bridging the gap between cryptographic assurance and the performance requirements of real-world blockchain applications.
X M Liu, Yilai Lian, Likai Jia, F H Wang · 8 authors
With the rapid development of the Internet of Vehicles (IoV), achieving trustworthy vehicle position verification while preserving location privacy has become a key requirement in intelligent traffic supervision scenarios such as defense control zones and urban restricted-access areas. Existing privacy-preserving schemes have difficulty simultaneously supporting accurate determination of complex-shaped prohibited areas and efficient computation, and still face malicious attacks such as interference with verification procedures, tampering with communication processes, and privacy inference when determining the positional relationship between vehicles and prohibited areas. To address these issues, this paper proposes an efficient privacy-preserving position verification (PPPV) scheme based on secure multi-party computation (MPC). The scheme supports arbitrary polygonal prohibited areas, including convex, concave, and self-intersecting polygons, thereby improving its applicability in complex IoV supervision scenarios. Based on an improved cross-product determination method, this paper constructs an efficient PPPV protocol under the semi-honest model, achieving near-plaintext computational efficiency while protecting the privacy of both vehicle locations and area boundaries. To resist malicious attacks, this paper further combines Paillier homomorphic encryption, the cut-and-choose method, and zero-knowledge proof to construct a secure PPPV protocol under the malicious model, which can effectively prevent protocol deviations, result tampering, and inference attacks. This paper also conducts formal security proof based on the real/ideal model paradigm, and evaluates the performance of the scheme through benchmark experiments and attack experiments. Experimental results show that the scheme achieves a good balance among efficiency, applicability, and security, providing a deployable trustworthy position verification mechanism for next-generation IoV intelligent supervision applications.
With the accelerated marketization of data factors, achieving fair contribution evaluation, privacy-preserving verification, and dynamic incentives in decentralized environments has emerged as a critical challenge. Existing studies exhibit a structural tension between privacy protection and verification transparency, while lacking adaptive mechanisms for non-independent and identically distributed (Non-IID) data scenarios. To address these issues, this paper proposes a collaborative trading framework integrating zero-knowledge proofs, personalized federated learning, and reinforcement learning. The framework employs zk-SNARKs to construct non-interactive proofs, thereby resolving the verification-privacy dilemma. A meta-learning–driven personalized aggregation scheme is introduced to correct valuation bias under Non-IID data distributions, and a deep Q-network (DQN) agent is deployed to enable dynamic incentive responses to market supply–demand fluctuations. Experiments conducted on Ethereum and Farcaster datasets demonstrate that the proposed mechanism improves the Contribution Fairness Index (CFI) by 19.7%–22.4% over the strongest baseline, achieving a Verification-Utility Ratio (VER) of 24.6. Under a collaboration scale of N = 20, market vitality entropy increases to 0.75 (baseline: 0.41), effectively suppressing monopolistic tendencies. Moreover, despite the introduction of proof mechanisms, the estimated additional on-chain verification and consensus latency per round is approximately 13 s, calibrated against empirical benchmarks. This work provides a verifiable trading mechanism for data factor markets that jointly ensures privacy, fairness, and efficiency, supporting secure data circulation in domains such as healthcare and finance.
Federated Learning(FL) is predominantly deployed in enterprise environments, where limited transparency and restricted auditability hinder broader adoption. Existing FL systems often suffer from opaque aggregation processes, making it unclear which model updates are accepted or discarded. Current mitigation strategies typically rely on external validators introducing additional computational and communication overhead. In this paper, we propose a novel FL framework that leverages existing Web3 technologies to enhance transparency, trust and auditability throughout the training process. The framework adopts a hierarchical architecture in which delegated managers orchestrate the FL training process within their respective federations. To mitigate adversarial and poisoning attacks, a combination of novelty detection and consensus mechanisms were employed. Model updates are encoded and broad casted to all managers, who independently evaluate their validity and those model updates that are approved by the consensus are incorporated into the global model. Additionally, a reputation score based backup mechanism is employed to ensure model generation. Extensive experiments conducted under real world scenarios demonstrate the effectiveness, resilience of the proposed framework, highlighting its potential to enable transparent FL beyond traditional enterprise setting.
Introduction Electronic health records (EHRs) are central to healthcare analytics, but their granularity increases re-identification risk when shared. Conventional privacy-preserving methods including k -anonymity, l -diversity, and differential privacy often protect confidentiality at the expense of analytical utility by weakening clinically meaningful correlations. Methods We propose SENTINEL-Chain, a blockchain-integrated privacy-preserving framework for secure EHR publishing. The privacy layer combines six mechanisms: Adaptive Correlation-Aware Perturbation (ACAP), Hierarchical Multi-Granularity Generalization (HMGG), Semantic-Aware Anatomization (SAA), Probabilistic Suppression with Utility Bounds (PSUB), Geo-Temporal Indistinguishability (GTI), and Ensemble Privacy Composition (EPC). The blockchain layer adds Merkle Hash Tree verification, PBFT-based validation, zero-knowledge proof compliance checking, and smart contract-based access control. Evaluation used a synthetic dataset (10,000 records) and two real clinical benchmarks (Wisconsin Breast Cancer, N = 569; Diabetes, N = 442). Results SENTINEL-Chain attains a privacy score of 79.9% and utility of 98.2%, producing a combined score of 178.1% that exceeds all 16 baselines by 4%-95%. Correlation fidelity reaches 99.9% for claim amounts, 99.6% for length of stay, 99.7% for age, and 99.1% for severity indices. The framework shows 100% resistance to record linkage attacks, with membership inference attacker advantage below the random guessing baseline. The blockchain layer processes 9,988 transactions in 101 blocks with complete integrity verification. Formal Renyi DP composition yields ε = 7.08 ( δ = 10 −5 ), and throughput reaches approximately 3,600 records/second up to one million records. Discussion SENTINEL-Chain addresses five identified gaps in healthcare data publishing: correlation destruction, the privacy-blockchain disconnect, single-technique brittleness, verification without disclosure, and limited attack resistance evaluation. Smart contract gas estimation on Ethereum indicates a per-record registration cost of 61,895 gas units; Layer-2 deployment would reduce costs by 10-100x.
Data privacy concerns have become more critical than ever as machine learning and applied intelligence systems permeate sensitive industries such as healthcare, finance, national security, and personal services. This necessitates the development of privacy-preserving strategies for protecting private information while retaining the utility of intelligent models. This survey provides a comprehensive overview of privacy-preserving machine learning, with an emphasis on the cryptographic and statistical methods that are transforming how safe learning systems are built. The study starts by examining the most important components of the machine learning model and figuring out which of these may be protected to solve important privacy problems. The article then explores modern cryptographic techniques, including homomorphic encryption, zero-knowledge proofs, secure multiparty computations, and a statistical approach called differential privacy, that support contemporary privacy-preserving machine learning solutions. The study then explores how these strategies are applied independently and in hybrid systems to achieve accuracy, efficiency, and balance of privacy. This survey provides promising direction for protecting sensitive information during real-world model training and inference, offering insights into the design of trustworthy applied intelligence systems.
In response to the difficulty of balancing privacy protection and system efficiency in energy data trading, this article analyzes the limitations of existing methods: static pseudonym mechanisms can easily lead to long-term identity link risks, traditional zk-SNARKs schemes have high computational overhead, and Raft consensus mechanisms lack robustness in adversarial environments. To address the above challenges, an integrated privacy protection scheme based on dynamic pseudonyms and lightweight zk-SNARKs is proposed. This scheme breaks the temporal correlation of transactions through a dynamic pseudonym generation mechanism, uses blockchain level batch processing proofs to reduce the computational and storage overhead of zero knowledge proofs, and introduces an LSTM based node health assessment model and incremental log synchronization mechanism to enhance the error tolerance and synchronization efficiency of the Raft consensus algorithm. The experimental results show that the proposed scheme outperforms traditional methods in terms of privacy, transaction processing performance, and system availability, effectively achieving a balance between privacy protection and operational efficiency, and providing a feasible technical path for energy data trading.
Frontier AI governance frameworks increasingly use cumulative training compute as the primary criterion for designating high-impact models, but enforcement rests on self-reporting because no technical verification primitive for training exists. Any future international agreement on frontier AI faces the same problem at higher stakes: coordinated regulation of technologies with significant externalities has historically rested on technical verification, without which agreements are declaratory. Recent governance analyses judge zero-knowledge proofs a promising candidate but currently impractical at frontier scale [26, 4]. We argue the impracticality is paradigm-bound rather than fundamental, and propose a verification architecture for frontier dense pre-training combining a pre-committed training specification, inter-node network observations, and on-the-fly Merkle commitments of intermediate computation, verified through a zero-knowledge Virtual Machine (zkVM) with native BF16/FP32 precompiles. The proof checks the actual floating-point computation the GPU performed rather than a fixed-point approximation, and preserves model-architecture confidentiality through a private training specification. The protocol produces three proof types: a genesis proof at initialisation, in-training step proofs across the run, and ex-ante attestations enforcing policy-relevant claims as running invariants, turning the training record into a governance-enforceable artefact. We estimate a deployable proof of concept within approximately 36 months at single-digit-percent training-side overhead, against a six-to-ten-year cycle for verification-grade custom silicon. Thirteen open research and engineering problems are catalogued as a research agenda for external contribution
Federated Learning (FL) is increasingly deployed in healthcare to enable collaborative intelligence while keeping sensitive data privately at local institutions. However, existing healthcare-oriented FL frameworks still suffer from several limitations: they are vulnerable to adversarial model updates, provide limited transparency and verifiable auditability, and often lack predictable performance under constrained resources. We present IIN-Health, a blockchain-enhanced intelligent fusion network tailored for dependable healthcare FL. IIN-Health adopts a dual-chain architecture with policy-aware access control and auditable provenance tracking to integrate learning, security, and governance in a unified framework. Evidence-Carrying Access Tokens (ECATs), combined with zero-knowledge proofs, are introduced to enforce patient-defined policies and validate access decisions without disclosing sensitive information. In addition, we design MedBFT-Δ, a domain-specific Byzantine fault-tolerant protocol that ensures reliable system behavior in the presence of faulty or malicious participants. We conduct several experiments to validate its feasibility and accuracy on the MNIST dataset. The results demonstrate that IIN-Health achieves smooth and stable convergence, exhibits strong resilience against poisoning attacks, and maintains graceful performance degradation under resource constraints, while preserving verifiable auditability of model updates and data flows. These observations indicate that IIN-Health can provide a practical balance among performance, security, and regulatory compliance, and thus offers a promising foundation for trustworthy and scalable FL deployments in healthcare.
Guilin Guan, Yang Cao, Zhenqiang Xie, Shu Yang · 5 authors
Abstract Industrial Internet of Things (IIoT) devices continuously generate large volumes of privacy-sensitive operational data. Federated Learning (FL) enables distributed model training without exposing raw data to external parties. However, existing FL solutions suffer from critical limitations, including single points of failure from centralized servers, insufficient verifiable defenses against gradient poisoning attacks, and poor adaptability to dynamic device churn. To address these challenges, we propose DVFL-IIoT, a fully decentralized and dynamic secure aggregation protocol tailored for IIoT environments. Our framework eliminates centralized trust assumptions using Pedersen Distributed Key Generation (DKG), supports seamless device joining and leaving without full system reinitialization through a key insulation mechanism, and ensures end-to-end verifiability via dual non-interactive zero-knowledge proofs (NIZKs). Formal security analysis proves that DVFL-IIoT achieves IND-CCA2 privacy, information-theoretic collusion resistance, and computational verifiability. Extensive experiments on two real-world IIoT intrusion detection benchmarks, ToN-IoT and Edge-IIoTset, achieve test accuracies of 98.81\% and 98.35\%, respectively, significantly outperforming state-of-the-art methods while maintaining strong robustness against poisoning attacks and dynamic device churn.
Abstract The high rate of healthcare digitalization and the extensive use of electronic healthcare records (EHRs) have heightened the issues of data privacy, interoperability, and the ability of the system to scale. Blockchain has emerged as a promising paradigm of decentralizing trust and improving security in healthcare information systems, and its real implementation is still divided. The paper highlights a systematic review of 94 peer-reviewed articles published in the years 2019–2025, which investigate architectural designs, privacy designs, scalability designs, federated learning designs, cross-chain interoperability designs, and novel cryptography designs in health care blockchain systems. The review contributes to the research in two ways: (i) a full taxonomy of blockchain design methods of healthcare applications and (ii) an organized discussion of the gaps in the research and future trends. The results demonstrate that hybrid constructions of lightweight zero-knowledge proofs, federated learning, adaptive consensus mechanisms, and cross-chain frameworks have better potential in privacy, scalability, and regulatory compliance than blockchain-based EHR solutions.
Foundational agent interoperability standards, notably the Agent-to-Agent (A2A) protocol and the Model Context Protocol (MCP), have advanced multi-agent system communication, and complementary identity frameworks leveraging W3C Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs) provide cryptographic agent authentication. However, no existing protocol supports content-based semantic routing of agent payloads across organisational trust boundaries without requiring the routing intermediary to decrypt the payload, which is a hard constraint in compliance-sensitive environments governed by GDPR, HIPAA, and MiFID II. We propose SS-ZKR, a three-mechanism privacy-preserving routing protocol designed as a complementary layer atop A2A/MCP. Mechanism I introduces blind routing via differentially private semantic intent vectors cryptographically bound to zero-knowledge proofs of payload-schema consistency. Mechanism II offers vector-weighted adaptive payload sanitisation with formal (epsilon, delta)-differential privacy for numerical fields and heuristic semantic aggregation for textual fields. Mechanism III presents a spatial-to-cryptographic policy compiler that translates visually defined trust-zone topologies into deterministic zero-knowledge access circuits. We provide a formal threat model, analyse information leakage bounds of intent vectors, present pseudocode for all three mechanisms, and give analytical complexity comparisons against TEE-based and homomorphic encryption-based routing baselines. SS-ZKR lets enterprises in financial services, healthcare, and defence orchestrate heterogeneous AI agents across regulatory boundaries without exposing proprietary data to routing infrastructure.
Large-language-model (LLM) inference is increasingly concentrated in dedicated GPU data centres and closed API platforms, raising barriers for institutions that want to run, study, or contribute to AI infrastructure. We argue that democratizing inference requires an architecture in which smaller organizations can participate as operators, builders, and researchers rather than only as customers. We propose OpenPrism Network, an open, UMA-first distributed inference architecture in which transformer layers are statically owned by nodes so that weights remain resident and only activations transit the network; a blockchain layer is restricted to settlement, reputation, and payment and never to compute; output integrity is established by multi-node redundancy with tolerance-banded fingerprinting rather than zero-knowledge proofs; and routing is locality-aware, keeping inference within metro-area clusters. The network is explicitly scoped to batch- and throughput-oriented, latency-tolerant workloads. We describe two deployment models: a distributed mesh harvesting idle institutional capacity, and a purpose-built UMA micro data center deployable by resource-constrained organizations as a sovereign inference facility. We also describe an open participation model in which node operators, runtime implementers, benchmark maintainers, and application integrators can contribute through published interfaces and open-source reference components. This is a position and architecture paper: we claim no original experimental results, and all quantitative figures are drawn from publicly available benchmarks and published specifications, cited explicitly. We report performance per watt honestly, including the threefold cost of consensus, and find that UMA nodes lose on operational efficiency against batched data-centre GPUs in the scoped regime; the architecture's advantage is therefore established on capital in the harvested-capacity model, participation, and data sovereignty, while total cost of ownership for the purpose-built micro data center is mixed and strongly pricing-regime dependent, not universally favorable. We frame two problems as genuinely unsolved: a consensus protocol for ML output verification under floating-point non-determinism, and a dynamic layer-assignment protocol that rebalances ownership as nodes join and leave without full weight redistribution. We also state a concrete validation roadmap, including prototype scope, baselines, and evaluation metrics.
Background Cross-border credit recognition in Sino-Foreign Cooperative Education (SFCE) suffers from data fragmentation, regulatory conflicts (e.g., GDPR vs. China’s Data Security Law), and low efficiency. Objective This paper proposes ZkHybridChain, a dual-layer blockchain credit bank (BCB) framework to resolve the privacy-compliance-efficiency trilemma. Methods The hybrid architecture integrates Polygon zkEVM (public credential hashing) and Hyperledger Fabric (private raw data storage). Zero-Knowledge Proofs (ZKP) and three-tiered smart contracts enable automated credit conversion (ECTS↔CNQF) and privacy-preserving verification. Results Experiments on 10,000 SFCE records show 58% efficiency gain (full lifecycle from ∼1,200 s to <9 s), cross-border latency <9 s, throughput up to 1,620 TPS, and ZKP verification latency 135 ms (93.7% success rate). Conclusion ZkHybridChain provides a scalable, GDPR/DSL-compliant solution for global education trust networks. Future work includes post-quantum cryptography and lightweight client protocols.
AgisFL v5.0 Autonomous Federated Learning Ecosystem Citation: Yadav, A. (2026). AgisFL v5.0: Autonomous Federated Learning Ecosystem with Privacy-Preserving Explainability and Enterprise AI Orchestration. Zenodo. https://doi.org/10.5281/zenodo.20363208 Table of Contents Executive Summary Abstract Introduction Industry Challenges in Federated Learning Research Objectives Literature Review System Overview Core Architectural Design Autonomous AI Engine Federated Learning Core Three-Line Integration Framework Security Architecture Privacy Preservation Framework Federated Explainability System Concept Drift Detection and Adaptive Retraining Distributed Systems Design Enterprise Governance Layer Monitoring and Observability API and Communication Architecture Database and Storage Infrastructure CI/CD and Release Engineering Kubernetes and Cloud Deployment Architecture Threat Modeling and Adversarial Defense Real-World Industry Applications Benchmarking and Performance Evaluation Comparative Analysis Scalability and Reliability Engineering Testing and Validation Framework Compliance and Regulatory Readiness Research Contributions Limitations Future Research Directions Conclusion References Appendices 1. Executive Summary AgisFL v5.0 is a next-generation autonomous federated learning ecosystem engineered to redefine how distributed artificial intelligence systems are developed, deployed, optimized, governed, and scaled in enterprise environments. The platform introduces a unified architecture that combines: Autonomous AI orchestration Federated machine learning Privacy-preserving analytics Enterprise governance Federated explainability Real-time monitoring Distributed optimization Security-first infrastructure Zero-trust operational principles Developer-centric integration abstractions Modern federated learning systems frequently suffer from fragmented tooling, operational complexity, difficult deployment procedures, weak observability, limited explainability, and insufficient enterprise governance. AgisFL addresses these limitations through a fully integrated ecosystem capable of autonomous optimization, adaptive retraining, drift monitoring, federated explainability, and production-grade orchestration. A major innovation introduced in AgisFL v5.0 is the Three-Line Integration SDK, which reduces federated learning implementation complexity from hundreds of lines of orchestration code into a simplified developer abstraction requiring only three operational commands. AgisFL also introduces: FedNAS (Federated Neural Architecture Search) FedHPO (Federated Hyperparameter Optimization) AutoFL autonomous orchestration engine Federated SHAP explainability framework Real-time drift detection systems Enterprise governance tooling Distributed observability infrastructure Autonomous retraining pipelines Integrated red-team simulation systems The platform is designed to support enterprise-grade deployments across: Healthcare AI Banking and fraud detection Cybersecurity analytics Autonomous transportation systems Industrial IoT ecosystems Smart infrastructure Defense intelligence systems Cross-organizational research networks AgisFL transforms federated learning from a research-heavy distributed systems problem into an operational autonomous AI platform suitable for enterprise production environments. 2. Abstract Federated learning has emerged as one of the most important paradigms in modern artificial intelligence because it enables collaborative machine learning without centralized raw data collection. Despite significant advances in federated optimization algorithms, practical enterprise adoption remains constrained by engineering complexity, infrastructure fragmentation, weak observability, insufficient explainability, operational overhead, and inadequate governance tooling. This paper introduces AgisFL v5.0, an enterprise-grade autonomous federated learning ecosystem designed to simplify distributed AI development while preserving privacy, scalability, explainability, and enterprise operational resilience. The proposed architecture integrates autonomous orchestration, federated neural architecture search, hyperparameter optimization, differential privacy, federated explainability, real-time telemetry, adaptive retraining, distributed governance, and multi-tenant deployment capabilities into a unified operational platform. A key contribution of this work is the introduction of a Three-Line Integration abstraction layer that reduces federated learning implementation complexity by approximately 98%, enabling developers to operationalize distributed machine learning workflows with minimal infrastructure overhead. Experimental evaluation demonstrates: Significant reduction in deployment complexity Faster convergence behavior Enhanced privacy guarantees Improved operational resilience Lower infrastructure overhead Enhanced governance visibility Autonomous optimization capabilities Enterprise-grade scalability The findings suggest that federated learning ecosystems can evolve beyond isolated research frameworks into fully autonomous enterprise-operational AI infrastructures capable of supporting large-scale real-world deployments. 3. Introduction Artificial intelligence systems increasingly depend on access to large-scale distributed datasets. However, centralized data aggregation introduces major concerns related to: Privacy Regulatory compliance Infrastructure cost Data ownership Security risk Cross-border governance Operational complexity Federated learning addresses these concerns by enabling decentralized collaborative model training where data remains localized while model updates are aggregated centrally or hierarchically. Despite its promise, enterprise adoption of federated learning remains limited due to several fundamental issues: 3.1 Complexity of Distributed Orchestration Traditional federated learning infrastructures require: Client synchronization systems Custom networking layers Aggregation orchestration Distributed storage pipelines Manual security implementation Complex deployment workflows These systems introduce substantial engineering overhead. 3.2 Limited Explainability Most federated learning frameworks prioritize optimization performance while neglecting explainability and interpretability requirements. This creates significant barriers in regulated domains such as: Healthcare Finance Cybersecurity Defense 3.3 Weak Enterprise Governance Existing systems frequently lack: Auditability Compliance tooling Enterprise observability Governance automation Operational telemetry Real-time incident response 3.4 Operational Fragility Distributed environments are inherently dynamic. Existing federated systems rarely support: Autonomous retraining Drift adaptation Self-healing infrastructure Dynamic client balancing Adaptive optimization AgisFL v5.0 was designed specifically to address these challenges. 4. Industry Challenges in Federated Learning 4.1 Data Sovereignty Constraints Modern organizations operate under increasingly strict regulatory environments including: GDPR HIPAA PCI-DSS ISO 27001 SOC2 NIST frameworks Centralized AI architectures frequently violate data locality requirements. 4.2 Security Risks Federated systems are vulnerable to: Model poisoning Data poisoning Gradient inversion attacks Membership inference attacks Byzantine participants Adversarial manipulation 4.3 Infrastructure Fragmentation Organizations often rely on heterogeneous environments: Cloud providers On-premise systems Edge devices Hybrid deployments Multi-region clusters This creates interoperability challenges. 4.4 Operational Scalability Large federated ecosystems require: Distributed orchestration Fault tolerance Client balancing Scheduling systems Autonomous optimization Resource-aware coordination 5. Research Objectives The primary research objectives of AgisFL v5.0 include: Objective 1 — Simplification Reduce federated learning deployment complexity through abstracted developer interfaces. Objective 2 — Autonomous AI Operations Enable self-optimizing distributed AI infrastructure. Objective 3 — Privacy Preservation Maintain strong privacy guarantees without sacrificing operational intelligence. Objective 4 — Explainability Provide interpretable federated learning workflows. Objective 5 — Enterprise Governance Introduce scalable governance and observability tooling. Objective 6 — Production Readiness Support real-world enterprise deployment scenarios. 6. Literature Review Federated learning was initially formalized by Google researchers to enable collaborative learning across decentralized mobile devices. Subsequent frameworks introduced: FedAvg optimization FedProx adaptive training Differential privacy systems Secure aggregation protocols Decentralized optimization methods However, existing systems frequently remain research-oriented. 6.1 Existing Framework Limitations Platform Limitation TensorFlow Federated Research-focused complexity Flower Limited autonomous optimization PySyft Operational deployment complexity OpenFL Limited explainability integration FedML Weak governance tooling AgisFL differentiates itself through autonomous orchestration, explainability integration, enterprise governance, and simplified deployment abstractions. 7. System Overview AgisFL v5.0 is composed of multiple inte
Federated Learning (FL) enables collaborative model training across decentralized data silos without raw data exchange, making it particularly attractive for privacy-sensitive domains like financial fraud detection. However, FL introduces critical vulnerabilities, notably the poisoning of global models through malicious client updates. Traditional defense mechanisms often rely on computationally expensive aggregation rules or complex anomaly detection. This paper introduces PoSFedFraud, a robust framework that integrates a Proof-of-Stake (PoS) economic layer directly into the federated aggregation process for financial fraud detection. By combining staking mechanisms with a dynamic reputation system, PoS-FedFraud economically disincentivizes adversarial behavior through automatic slashing and trust decay. We simulate a toy fraud detection scenario using a 29-dimensional feature space, demonstrating how the framework defends against norm-based gradient poisoning attacks. Our experimental results show that PoS-FedFraud successfully identifies and penalizes malicious actors—reducing their stake and trust upon detection—while maintaining global model convergence. The proposed method offers an incentive-compatible punitive layer that complements existing robust aggregation and anomaly-detection techniques for decentralized financial applications.
Artificial Intelligence (AI) is changing the healthcare industry by automating the process of medical records, diagnostic, and clinical decision-making. Nevertheless, the growing dependence on data-driven systems is of concern in terms of patient privacy, data integrity, and system security. To deal with these challenges, blockchain technology, in combination with smart contracts, can be used as a decentralized and transparent solution. The paper is a systematic review of the current developments in smart contract frameworks based on blockchain technologies and safe AI-driven healthcare systems. The review addresses four key aspects, which include data privacy preservation, secure information exchange, decentralized access management, and AI-blockchain integration. The recent technologies like federated learning, zero-knowledge proofs, and deep learning-based blockchain validation are examined in terms of their contribution to the improvement of patient confidentiality and system-level security. The paper also assesses a suggested multi-layered infrastructure combining artificial intelligence processing, authorized blockchain, and role-based smart contracts. The comparison indicates that the federated learning approach with permissioned blockchain has an optimal security- versus-computational efficiency ratio. The review establishes that smart contract automation has a substantial potential to benefit healthcare data governance, but notes that scalability, interoperability and energy efficiency issues remain a challenge. This overall overview confirms the potential of blockchain and smart contracts to facilitate dependable, most open, and efficient healthcare frameworks that may meet the security and privacy requirements of the contemporary healthcare setting.
Zuodong Wu, Dawei Zhang, Mianxiong Dong, Kaoru Ota
The General Data Protection Regulation (GDPR) aims to enable the free flow of personal data while enhancing individual control. Integrating privacy-preserving data retrieval methods can provide stronger protection for personal privacy. However, existing approaches lack compliance mechanisms aligned with the GDPR, making it difficult in practice to simultaneously satisfy the principles of lawfulness and data minimization, while also exhibiting clear limitations in both security and efficiency. To address these problems, we propose VOLE-PDRAA, an efficient privacy-preserving data retrieval protocol with anonymous authorization based on the Vector-OLE (VOLE). Specifically, VOLE-PDRAA constructs a VOLE-blinded identifier by integrating pseudorandom linear encoding with VOLE-derived correlation vectors, enabling rigorous anonymity guarantees during authorization. Building on this, the protocol incorporates a non-interactive zero-knowledge proof (NIZK) to achieve anonymous authorization for the data subject and to generate verifiable informed consent proofs, thereby meeting the principle of lawfulness. Meanwhile, the data controller can verify whether each retrieval request falls within the scope authorized by the data subject without learning any identifiable information, thus maintaining adherence to the data-minimization principle in a post-quantum environment. Furthermore, VOLE-PDRAA utilizes labeled private set intersection (labeled-PSI) to safeguard the confidentiality of identifiers and their associated records under post-quantum security conditions, while enabling large-scale batch retrieval. Our protocol takes a comprehensive security analysis within the Universal Composability (UC) framework. Experimental evaluation validates its superiority through comparison with state-of-the-art work.
As large language models (LLMs) grow in scale and are predominantly served from remote platforms, verifying faithful inference execution becomes critical (i.e., ensuring that a provider actually executes the advertised model and computational workload rather than a tampered or downsized variant). Zero-knowledge (ZK) LLM inference offers an appealing approach. It promises public verifiability and delivers per-instance guarantees of equational correctness by proving that an output is consistent with executing a public architecture under committed, private weights. Though, we show that it does not bind the effort expended to produce the output. In this paper, we formalize this overlooked effort gap and introduce the Hollow-LLM Attack, in which a dishonest provider retains the declared architecture and parameter count but embeds ghost weights whose algebraic structure collapses effective computation. These witnesses satisfy the verification circuit and yield valid proofs, even though the dishonest model owner, who serves as the prover, performs computation commensurate with a much smaller model than the declared public architecture. This creates a profitable equilibrium in which providers deliver provably correct outputs at small-model cost while overclaiming model size. Accordingly, we characterize concrete families of ghost weights that compose with standard transformer blocks and show that such hollow deployments substantially reduce serving cost with zero quality loss under the same verification circuit. These findings underscore that proof of correct inference is not proof of large-model execution and necessitate additional protections to bind correctness to verifiable computational work.
The tremendous progress of medical foundation models has proven to be groundbreaking in meta-analysis of clinical prediction, diagnosis, and multimodal healthcare analytics, but the development of medical foundation models is limited due to stringent data privacy concerns, cross-institutional trust issues, and security risks in a collaborative learning environment. Traditional federated learning allows for distributed training of the model with no central sharing of data but is prone to poisoning of the model, inference attacks, and low verifiability of participating institutions. This study proposes an idea of Autonomous Trust and Zero-Knowledge Blockchain Framework (AT-ZKBF) for Federated Medical Foundation Models, to establish decentralized trust, cryptographic verifiability and secure collaboration among heterogeneous healthcare providers. The framework combines the foundation model training in a federated peer-to-peer setup, the permissioned blockchain network for trust orchestration and mechanisms using the zero-knowledge proof (ZKP) for model updates to avoid the content of sensitive parameters of the model. Every local update is cryptographically authenticated with zk-SNARK-based zero-knowledge proofs that check proper gradient descent running and limited limit on updates without exposing private gradients or data. A reputation-driven trust scoring module automatically scores the reliability of participants. Experimental evaluation done on a BraTs, a multi-institutional medical imaging dataset shows that the proposed framework can get 96.4% classification accuracy (up 4.8% vs. standard federated learning) with poisoning model control decreased by 63% and communication overhead reduced by 21% by optimized blockchain batching. Security analysis makes sure of the robustness from gradient inferences and Byzantine attacks. The validation upon integration of autonomous trust computation, and zero-knowledge cryptography to blockchain enabled federated learning substantially adds to security, transparency and scalability for collaborative medical foundation model training providing a probable way forward to privacy preserving trust worthy AI in healthcare ecosystems.
The rapid evolution of Decentralized Finance (DeFi) has introduced unprecedented financial innovations alongside complex fraud vectors that challenge conventional security mechanisms.Traditional fraud detection systems rely heavily on centralized data aggregation and opaque machine learning models, which are fundamentally incompatible with the decentralized and trust-minimized architecture of blockchain ecosystems.Emerging paradigms such as Federated Learning (FL) and Explainable Artificial Intelligence (XAI) have been independently proposed to address privacy and transparency concerns in financial systems.However, despite significant progress in each domain, the literature reveals methodological fragmentation and architectural disconnection among blockchain-based fraud detection, privacy-preserving learning, and explainability mechanisms.This study critically reviews existing research on traditional finance fraud detection, blockchain analytics, federated learning security, XAI applications, and blockchain-FL integration frameworks.Through comparative and analytical synthesis, it identifies critical research gaps, including the absence of unified decentralized fraud architectures, insufficient explainability in on-chain systems, and limited governance models for federated financial intelligence.This study establishes a theoretical and technological foundation for an integrated blockchain-driven FL-XAI framework tailored for DeFi fraud detection.