Effective management of education funding is crucial to ensuring the quality and sustainability of education, particularly in secondary schools, which often face significant financial challenges. This study aims to understand the meaning of technical and allocative efficiency in education funding in secondary schools in the era of decentralization. Using a qualitative approach, the study explores the experiences, perceptions, and strategies of school stakeholders including principals, teachers, and financial managers in managing educational resources. Data .were collected through in-depth interviews, observations, and document analysis, then analyzed thematically to uncover emerging patterns and meanings. The results indicate that technical efficiency is understood as a school's ability to maximize the use of funds to support effective teaching and learning, while allocative efficiency is defined as the alignment of budget distribution with educational priority needs and the local context. The era of decentralization provides space for schools to be more independent in decision making, but also poses challenges in maintaining a balance between resource constraints and demands for quality improvement. These findings emphasize the importance of managerial capacity and transparency in education funding and provide implications for policies that promote accountability and equitable access to education at the regional level.
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
Blockchain technology has provided the transformation of a decentralized system as the concept can make transparency, immutability, and security available without centralized authorities. However, standard algorithms of consensus such as Proof-of-Work (PoW) consume excessive resources and energy with the cost of sustainability, and limiting the scalability of the blockchain and its performance in the environment. The energy efficient consensus algorithm has turned out to be an axiom in limiting the challenges and also protecting the network security and, performance. They are Proof-of-Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), Delegated Proof-of-Stake (DPoS), hybrid consensus and adaptive validation techniques to reduce energy consumption and enhance throughput. Recent work has been done on streamlining selections of the validators to be more efficient, minimize pointless calculations and integrate crafty resource control in order to enhance the performance of consent [15]. It is presumed in the study that energy efficiency research will be conducted through consensus research consolidation based on adaptive validation, participation and weighted node of the consensus strategy that allows optimization in terms of sustainability. The methodology evaluates the energy consumption, throughput and latency and scalability together on behalf of simulated blockchain environments. The facts of the experiment results indicate that the specified framework can be used to reduce the number of energies consumed and guarantee the high degree of security and performance. The results confirm that the implementation of optimal consensus algorithms can be used to provide sustainable blockchain in such tools as IoT, healthcare, and supply chain. The current research contributes to the development of environmentally safe blockchain chains on an efficient consensus innovation.
This conceptual paper explores the profound impact and pivotal role of information systems (IS) within the rapidly evolving landscape of Decentralized Finance (DeFi). Emerging from the advancements in blockchain technology, DeFi represents a paradigm shift in financial management, offering an ecosystem that is more inclusive, transparent, and efficient by removing centralized intermediaries through smart contracts. This paper analyzes how IS principles are fundamental to the design, management, and security of DeFi protocols, contrasting them with traditional financial systems. It delves into core DeFi applications such as Decentralized Exchanges (DEXs), lending/borrowing protocols, stablecoins, and yield farming, emphasizing their underlying IS architectures and the challenges related to user experience (UX/UI). Furthermore, the paper discusses critical IS aspects in DeFi, including security management, automation via smart contracts, blockchain-based analytics for risk management and anomaly detection, and the unique governance mechanisms through Decentralized Autonomous Organizations (DAOs). Finally, it outlines the future trajectory of DeFi, considering its integration with emerging technologies like Artificial Intelligence (AI) and Web3, and its evolving relationship with global financial systems and regulations. This work contributes to understanding the complex interplay between technology and finance, highlighting how robust information systems are indispensable for DeFi's sustained growth and its potential to reshape the digital financial ecosystem.
Federated learning is used in medical imaging where privacy prohibits centralizing data. Standard federated algorithms assume homogeneous hardware, identical architectures, and centralized aggregation, which fails when hospitals have unequal compute resources. We propose capacity-aware coordination: measure each hospital's throughput, assign capacity-appropriate architectures (MobileNetV3-Small, EfficientNet-B0, ResNet-50), and combine predictions via weighted ensemble. Weak and strong hospitals can participate without forcing uniform architectures. We separate on-chain policy from off-chain learning. A Solidity contract stores hospital registration, benchmark hashes, metrics, and weights. Hospitals train locally and submit only hashes and scalars (not parameters). Weighted ensemble inference is computed off-chain. Experiments on PneumoniaMNIST and DermaMNIST (5 seeds, 3 non-IID levels) show our method achieves lower or equal calibration error versus equal-weight ensemble and competitive accuracy versus FedAvg, FedProx, and FedMD. Communication overhead is 224 bytes per round, a reduction of over 912,000x compared to FedAvg.
This Present Study Topic is ‘The Influence of Data Structures on Optimal Algorithm Design and Performance in Fintech’ The efficient data structures play a critical role in improving algorithm design, computational speed, scalability, and memory optimisation within fintech systems. Recent fintech studies emphasise that modern financial platforms process massive real-time transactional data, requiring optimised algorithms supported by advanced data structures such as trees, graphs, hash tables, heaps, and distributed ledgers. Financial Technology applications, including digital banking, fraud detection, blockchain, algorithmic trading, and risk management, rely heavily on these computational techniques to maintain performance and security. Artificial Intelligence and reinforcement learning demonstrated that optimal algorithm design supports decision-making, portfolio optimisation, fraud detection, and automated trading systems. Researchers concluded that the integration of suitable data structures with intelligent algorithms improves prediction accuracy, computational efficiency, and operational scalability in fintech applications. These technologies are becoming increasingly important in modern digital financial ecosystems driven by big data and real-time analytics.
The global trade finance ecosystem, long characterised by manual documentation, multi-layered intermediation, and protracted settlement cycles, is undergoing a profound structural transformation through the adoption of blockchain-based smart contracts. This article examines two principal objectives: (1) the extent to which smart contracts automate traditional trade finance processes, and (2) the degree to which they reduce systemic dependency on financial and documentary intermediaries. Drawing upon peer-reviewed scholarship, institutional reports, and empirical findings published between 2022 and 2025, the study undertakes a critical analysis of the operational, economic, legal, and societal dimensions of this technological shift. Findings indicate that while smart contracts demonstrably compress settlement cycles, reduce transaction costs, and enhance transparency, significant challenges persist concerning legal enforceability, regulatory fragmentation, and cybersecurity vulnerability. The article concludes with implications for policymakers, financial institutions, SMEs, and society at large.
Xiao-Yang Liu Yanglet, Xiaodong Wang, Agostino Capponi
We argue that trustworthy AI agents, especially in high-stakes and policy-governed domains, should make execution conditional on certified traces rather than rely only on stronger generative models, output-level guardrails, or post-hoc audits. A generative agent may propose recommendations, tool calls, reports, or actions, but generation is not permission: an action may be computable yet impermissible, and individually permissible actions may compose into an impermissible trace. We formalize trustworthy agency through a \textbf{Proposal--Certification--Execution (PCE)} architecture: a probabilistic generating machine $M_G$ proposes candidate execution traces, a \textbf{Permissibility Machine} $M_Π$ certifies proposed traces under a policy system $Π$, and execution proceeds only for certified traces. The executable trace language is $L_{\mathrm{exec}} = L_G \cap L_{\mathrm{cert}}(M_Π)$. Before execution, a trace is a structured pre-execution record submitted for certification: it specifies intended steps, evidence, proposed tool calls, approvals, replayable computations, credentials, and execution conditions. This perspective complements chain-of-thought monitorability: visible reasoning may help detect misbehavior, but monitorability is not certifiability, and reasoning is only one component of a broader execution trace. The formal principle is simple: an agent-generated trace should execute only when it carries a checkable certificate witnessing permissibility under $Π$: \textbf{no certificate, no execution}. We develop certified traces and Permissibility Machines as foundations for trustworthy AI agents, connect trace certification to proof-carrying execution, proof memory, privacy, and zero-knowledge certificates, and propose evaluating agents by what generated traces can be safely certified for execution, not by output accuracy alone.
Intellectual Property (IP) transactions play a vital role in the contemporary global economy, encompassing the exchange of intangible assets such as patents, copyrights, trademarks, and trade secrets. These assets are fundamental drivers of innovation and economic development across diverse industries. However, conventional methods of managing IP transactions are often characterized by inefficiency, high transaction costs, lack of transparency, and frequent disputes arising from ambiguities in enforcement and contractual obligations. This study examines the potential of blockchain-based smart contracts to address these challenges by enhancing efficiency, fairness, and transparency in IP transactions. Smart contracts, which are self-executing agreements encoded in computer-readable protocols, facilitate automated execution of predetermined contractual terms without requiring intermediary intervention. The integration of blockchain technology with decentralized and secure ledger systems minimizes errors, reduces dependency on intermediaries, and mitigates disputes resulting from cumbersome and unclear procedural mechanisms in conventional IP transactions. Additionally, smart contracts streamline licensing, royalty distribution, and contract enforcement, thereby accelerating transaction processes while ensuring improved security and accountability. Blockchain decentralization further strengthens the protection of intellectual property transactions against unauthorized alterations. Smart contracts also support automated royalty allocation, enabling equitable payment distribution among creators, rights holders, and intellectual property owners. Transparency is enhanced through shared access to accurate transactional information, fostering trust among stakeholders and reducing the likelihood of legal conflicts. Despite these advantages, the adoption of smart contracts in IP transactions faces several practical and legal challenges, including regulatory recognition, enforceability across jurisdictions, compatibility with existing intellectual property frameworks, and privacy concerns associated with confidential transactional data. This article investigates how blockchain-integrated smart contracts can transform intellectual property transactions, with particular focus on improving efficiency, strengthening security, ensuring fair compensation, and promoting transparency. By examining relevant theoretical perspectives, case studies, and practical applications, the study offers insights into the broader implications of adopting blockchain technology for intellectual property management.
Blockchain-based crowdsourcing logistics is a promising decentralized paradigm for solving the “last-mile delivery” problem, in which smart contracts automatically execute the business logic. Since crowdsourcing logistics inherently involves frequent fund transfers, its smart contracts are particularly susceptible to reentrancy vulnerabilities. Existing works address reentrancy by inserting a lock mechanism at design-time, which lacks dynamic responsiveness and incurs additional gas overhead. To overcome this limitation, we propose RE4SC, the first runtime enforcement framework for vulnerable smart contracts. RE4SC contains two components: off-Blockchain granularity segmentation and on-Blockchain granular block reordering. At the off-Blockchain level, bytecode is segmented into granular blocks through control flow analysis. This yields a finer granularity than conventional basic blocks in a control flow graph. These granular blocks are then organized into a tree structure that captures their hierarchical nesting relationships. A data flow analysis further ensures data dependency consistency after reordering. At the on-Blockchain level, a runtime enforcer retrieves the pre-computed reordering specifications from off-Blockchain analysis. It applies a depth-first reordering algorithm to reposition key state variable assignments before transfer operations, eliminating reentrancy vulnerabilities without introducing additional bytecode. We implement a prototype tool and make it open-source. Experiments on self-constructed crowdsourcing logistics contracts and three public datasets demonstrate that RE4SC repairs vulnerable contracts with zero gas overhead, outperforming existing approaches.
The rapid adoption of multi-provider container orchestration has introduced critical vulnerabilities in chain-of-custody (CoC) management, where logs and provenance records remain fragmented across heterogeneous cloud environments with inconsistent trust models. This study proposes a quantum-resistant CoC framework integrating lattice-based post-quantum signatures and zero-knowledge proofs for verifiable and privacy-preserving provenance tracking. Experimental evaluation in a simulated Kubernetes multi-cloud environment achieved a tamper detection rate exceeding 99.98% with acceptable performance overhead. The framework aligns with GDPR, ISO/IEC 27001, and ISO/IEC 27037 standards, providing a robust foundation for forensic-grade provenance management in the quantum era.
This paper examines non-fungible tokens (NFTs) beyond speculative hype, focusing on their cultural value, labour structures, and prospects for long-term market stabilisation. It argues that NFTs function as technological primitives that enable the attribution, ownership, and exchange of digital cultural artefacts through blockchainbased provenance systems. While often framed primarily as financial instruments, NFTs also operate as cultural infrastructures that encode identity, community participation, and creative expression across art, music, gaming, and digital media ecosystems. The study highlights how value in NFT systems emerges not only from market dynamics but also from labour-intensive processes of creation, curation, and platform governance. It further explores how labour is distributed across artists, collectors, curators, developers, and communities, revealing complex hierarchies shaped by platform economies and speculative incentives. In addressing market stabilisation, the paper analyses liquidity, speculation, and price discovery as central mechanisms shaping NFT volatility, while also considering regulatory, institutional, and infrastructural developments that may support more sustainable ecosystems. Ultimately, it contends that NFTs should be understood as socio-technical systems where cultural meaning, economic exchange, and labour relations intersect, and that their long-term relevance depends on whether these dimensions can be balanced beyond cycles of hype and collapse. Keywords: Non-fungible tokens (NFTs), Cultural value; Digital labour, Blockchain provenance, and Market stabilization.
A tokenised energy market settles payment against metered dispatch, but the meter reading is the prosumer's private information: a self-interested prosumer can report more energy than it supplied and be paid for the difference. The companion papers in this programme assume meter integrity — truthful reporting — and build settlement, participation, and delivery contracts on top of it. This paper derives the verification contract that makes the assumption hold. A prosumer dispatches a quantity it observes privately and reports a possibly inflated figure to the settlement layer; the grid-telemetry layer can audit a report at a cost, detecting a discrepancy with a probability that reflects sensor accuracy, and a detected misreport forfeits a posted verification stake. We treat the audit probability, the stake, and the sensor accuracy as the designer's instruments and characterise the verification that makes truthful reporting weakly dominant at minimum cost. The baseline assumes a margin-independent detection probability and one-sided audit error (false negatives possible, false positives excluded); both are stated and the general margin-dependent condition is given. First, truthful reporting is weakly dominant if and only if the expected forfeiture covers the largest gain from admissible over-reporting, αφB ≥ Pm̄ (strict under strict inequality), where α is the audit probability, φ the per-audit detection probability, B the stake, and m̄ the largest admissible over-report; with a one-unit maximum this is αφB ≥ P (Proposition 1). Second, along this deterrence frontier the audit probability is α = Pm̄/(φB), and once the stake is itself chosen against its capital carry the least-cost interior contract is B* = √(κPm̄/(ρφ)), α* = √(ρPm̄/(κφ)), total cost 2√(ρκPm̄/φ), all decreasing in detection accuracy, so accurate telemetry drives the audit rate, the stake, and the cost down together (Theorem 1). Third, sensor accuracy is itself a procurable instrument with a convex capital cost, and the cost-minimising accuracy equates marginal sensor capital cost to the marginal audit-opex saving, a capex–opex frontier between better meters and more auditing (Proposition 2). Fourth, the per-report enforcement αφB is exactly the meter-integrity guarantee the companion papers assume; truthful reporting is weakly dominant on the binding frontier and strict under an arbitrarily small slack, so the reported quantity equals the dispatched quantity, discharging that assumption from primitives and closing the stack at its base (Proposition 3). Full proofs are in the online appendix.
In approximately the year 2000, the author conceived and partially implemented a multi-layered community economic system centered on Shibuya, Tokyo. The system integrated real-time human broadcasting, local media production, a unified community coupon currency, youth-driven cultural monitoring, and digital education — years before the terminology of "DAO," "Web3," "UGC," or "creator economy" existed. This paper documents that original conception, analyzes its structural architecture, and demonstrates its direct lineage to the author's current work: the Hikari Currency (光貨) ecosystem and the ECHO AI Artist platform. The Shibuya system was not understood by contemporaries. It is understood now.
This manuscript is a preprint that has been submitted to a peer-reviewed journal and is currently under review. It is shared for early academic dissemination and has not yet undergone final journal publication. The study presents a comprehensive comparative analysis of three widely used blockchain consensus algorithms: Proof of Work (PoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS). The analysis evaluates key performance factors including energy consumption, security, scalability (transaction throughput), decentralization level, transaction confirmation time, and real-world adoption rate. Based on findings from peer-reviewed literature and empirical on-chain data, PoW provides the highest level of security but has extremely high energy consumption (over 150 TWh annually). PoS significantly reduces energy usage by approximately 99.9% compared to PoW while maintaining security through economic incentives. DPoS offers the highest scalability in terms of transactions per second but introduces trade-offs in decentralization.
Daniel Cason, Gordon Liao, Sergio Mena, Nenad Milošević · 8 authors
Blockchain systems that settle financial transactions face a structural tension: the single validator that assembles each block holds unilateral power over transaction inclusion and ordering. Traditional markets curb this very power through front-running and market-manipulation laws. Regulators have flagged the absence of such rules as a first-order concern for blockchain-based financial infrastructure. In response, we introduce AMP, a multi-proposer protocol, on top of the Tendermint consensus algorithm, where no validator can control the flow of transactions into blocks. Instead, dedicated nodes called proposers sit between users and validators. They collect user transactions, group them into payloads, and broadcast the payloads to all validators. Consequently, there is no mempool, and AMP applies the design principle of separating dissemination from agreement, which can lead to higher throughput. Validators publicly attest to receiving payloads and run consensus to decide the set of payloads to include in the next block. When all correct validators attest to a given payload, AMP guarantees that payload will be included in the next block; a block thus contains payloads from multiple proposers, allowing for bulk finalization. This bounded inclusion guarantee along with a deterministic ordering algorithm which is run over all payloads included in a block, curbs the power of any single validator. Validators no longer control what is included in a block, nor can they arbitrarily order the contents of blocks.