Ruonan Chen, Ye Dong, Yizhong Liu, Tingyu Fan · 8 authors
Federated Learning (FL) is a distributed machine learning paradigm that allows multiple clients to train models collaboratively without sharing local data. Numerous works have explored security and privacy protection in FL, as well as its integration with blockchain technology. However, existing FL works still face critical issues. i) It is difficult to achieving poisoning robustness and data privacy while ensuring high model accuracy. Malicious clients can launch poisoning attacks that degrade the global model. Besides, aggregators can infer private data from the gradients, causing privacy leakages. Existing privacy-preserving poisoning defense FL solutions suffer from decreased model accuracy and high computational overhead. ii) Blockchain-assisted FL records iterative gradient updates on-chain to prevent model tampering, yet existing schemes are not compatible with practical blockchains and incur high costs for maintaining the gradients on-chain. Besides, incentives are overlooked, where unfair reward distribution hinders the sustainable development of the FL community. In this work, we propose FLock, a robust and privacy-preserving FL scheme based on practical blockchain state channels. First, we propose a lightweight secure Multi-party Computation (MPC)-friendly robust aggregation method through quantization, median, and Hamming distance, which could resist poisoning attacks against up to <50% malicious clients. Besides, we propose communication-efficient Shamir's secret sharing-based MPC protocols to protect data privacy with high model accuracy. Second, we utilize blockchain off-chain state channels to achieve immutable model records and incentive distribution. FLock achieves cost-effective compatibility with practical cryptocurrency platforms, e.g. Ethereum, along with fair incentives, by merging the secure aggregation into a multi-party state channel. In addition, a pipelined Byzantine Fault-Tolerant (BFT) consensus is integrated where each aggregator can reconstruct the final aggregated results. Lastly, we implement FLock and the evaluation results demonstrate that FLock enhances robustness and privacy, while maintaining efficiency and high model accuracy. Even with 25 aggregators and 100 clients, FLock can complete one secure aggregation for ResNet in 2 minutes over a WAN. FLock successfully implements secure aggregation with such a large number of aggregators, thereby enhancing the fault tolerance of the aggregation.
The rise of digital healthcare has intensified concerns over data privacy, particularly in cross-institutional medical data exchanges. This study introduces a blockchain-based protocol leveraging Zero-Knowledge Proofs (ZKP), specifically zk-SNARK, to enable verifiable yet privacy-preserving health data sharing. Built on a permissioned Ethereum blockchain, the protocol ensures that medical data validity can be confirmed without disclosing sensitive content. System implementation involves Python-based zk-circuits, smart contracts in Solidity, and RESTful APIs supporting HL7 FHIR formats for interoperability. Performance evaluations show promising results: proof verification times remained under 100 ms, with average proof sizes below 2 KB, even under complex transaction scenarios. Gas consumption analysis indicates a trade-off—ZKP-enabled transactions consumed approximately 93,000 gas units, compared to 52,800 in baseline cases. Interoperability testing across 10 FHIR-based scenarios resulted in 100% parsing success and an average data integration time of 1.7 seconds. Security assessments under white-box threat models confirmed that sensitive information remains unreconstructable, preserving patient confidentiality. Compared to previous implementations using zk-STARK, this protocol offers a 30% improvement in verification efficiency and a 45% reduction in proof size. The novelty lies in combining lightweight ZKP mechanisms with an interoperability-focused design, tailored for realistic hospital infrastructures. This research delivers a scalable, standards-compliant architecture poised to advance secure digital healthcare ecosystems while complying with regulations like GDPR
A. Althaf Ali, M. A. Gunavathie, V. Srinivasan, M. Aruna · 6 authors
The integration of smart city applications with healthcare has revolutionized patient monitoring and medical data management. However, ensuring the privacy and security of Electronic Health Records (EHR) remains a critical challenge, especially in IoT-based environments with resource-constrained devices. This paper proposes a novel Blockchain-Enabled Federated Learning (BFL) framework to enhance privacy preservation in EHR processing. The proposed framework leverages zero-knowledge proofs (ZKP) for authentication and homomorphic encryption for secure computation, ensuring robust data security without exposing raw patient data. Federated Learning (FL) enables decentralized model training across IoT devices, reducing privacy risks while maintaining data utility. Additionally, blockchain technology enhances the integrity and transparency of EHR transactions by creating a tamper-proof ledger. The performance of the proposed BFL framework is evaluated based on data utility, model accuracy, execution time, and scalability across varying sizes of EHR datasets. Results demonstrate improved privacy preservation, reduced computational overhead, and enhanced model efficiency, making it a promising approach for secure and privacy-aware IoT-based smart healthcare systems.
The rising energy demands of large data centers call for energy-efficient AI training methods. Federated Learning (FL), a decentralized paradigm, offers a solution by enabling model training across distributed devices without centralizing sensitive data. This review explores FL's integration with distributed data centers to achieve energy efficiency, analyzing methods like federated averaging and energy-aware protocols to minimize resource use. It highlights techniques such as model compression, quantization, and adaptive FL to reduce on-device computation while maintaining performance. Practical implementation is discussed through tools like TensorFlow Federated and PySyft, with case studies from healthcare, finance, and IoT showcasing cost reductions and sustainability. Future research directions include combining FL with edge computing and low-power AI hardware, emphasizing FL's potential for scalable, sustainable AI.
Joy Nnenna Okolo, Adesola Adul-Gafar Arowogbadamu, Samuel Adetayo Adeniji, Rhoda Kalu Tasie
The rapid adoption of mobile AI applications in areas such as healthcare, finance, and personalized services has raised significant concerns about data privacy and security. Traditional centralized machine learning (ML) models require mobile devices to transmit user data to cloud servers, posing risks of data breaches and regulatory non-compliance. Federated learning (FL) addresses these concerns by allowing decentralized AI model training directly on user devices, ensuring that raw data remains private and never leaves the device. However, FL faces security vulnerabilities and performance limitations, including model inversion attacks, data poisoning risks, and high computational overhead. This paper explores key privacy-preserving techniques such as differential privacy, secure aggregation, and homomorphic encryption, which enhance FL security while maintaining model accuracy. Additionally, emerging trends such as blockchain-integrated FL, post-quantum cryptography, and AI-driven optimization are analyzed to highlight the future of privacy-preserving mobile AI ecosystems. By integrating advanced cryptographic techniques and decentralized verification mechanisms, FL can enable scalable, secure, and regulation-compliant AI applications, ensuring a balance between data privacy and AI innovation.
Damiano Di Francesco Maesa, Matteo Loporchio, Frank Tietze
This paper considers the application scenario of Intellectual Property (IP) management, a business process yet to fully embrace digitisation and the advantages it brings. We propose to leverage Distributed Ledger Technology (DLT) to digitise license agreements management by providing automated and trustworthy royalty computation, transaction execution, and payment distribution. This can be achieved by employing smart licenses, i.e., bundles of smart contracts implementing the royalty logic of license agreements. To provide scalability, flexibility, and resilience, we propose to deploy smart licenses on a network of networks model, i.e., a set of heterogeneous networks potentially running different DLT protocols and connected by cross-chain information exchange protocols. A novel advantage of the network of networks approach is that it allows for the use of private values for royalty computation, which is impossible in the traditional model. Of course, supporting private DLT networks requires privacy-preserving cross-chain schemes, a still open problem in the literature. This is why we present two alternative privacy-preserving cross-chain schemes for our considered application scenario of license agreements management, one based on Homomorphic Encryption (HE) and the other on Zero-Knowledge (ZK) proofs. Besides discussing their theoretical advantages and drawbacks, we present an experimental evaluation of a prototype implementation of smart licenses based on both schemes. • We propose a network of networks model that enhances scalability in smart license ecosystems. • The proposed model allows smart licenses to leverage data from heterogeneous networks, including private ones. • We compare two methods for privacy-preserving cross-chain communication: homomorphic encryption and zero-knowledge proofs. • We experimentally evaluate and compare two different privacy-preserving smart license implementations.
ABSTRACT Self‐sovereign identity management systems operate in open network environments and face security threats from semi‐trusted or malicious adversary models. In such environments, verifiable credentials are susceptible to attacks such as theft and forgery. In response to the privacy risks associated with verifiable credentials during issuance and revocation, this article proposes a privacy protection scheme for user information during the issuance and revocation processes of verifiable credentials in self‐sovereign identity management based on blockchain technology. First, a privacy‐preserving method that does not rely on a single identity provider and resists Sybil attacks has been designed using secure multi‐party computation cryptographic techniques. Second, the consortium blockchain committee nodes act as the issuer of verifiable credentials. By combining attribute commitments and zero‐knowledge proof techniques, the user's identity information is hidden, achieving the privacy protection goal during the issuance of verifiable credentials. Furthermore, in order to protect user privacy during the revocation of verifiable credentials (VCs), we employ a cryptographic accumulator technique to implement the revocation operation. This approach ensures the security of user privacy while effectively managing the revocation of credentials. Finally, this paper conducts a security analysis and performance evaluation of the proposed scheme. The results show that our scheme strikes a balance between security needs and time efficiency.
Michele Orrù, George Kadianakis, Mary Maller, Greg Zaverucha
A fundamental challenge in zero-knowledge proof systems is implementing operations that are “foreign” to the underlying constraint system, in that they are arithmetic operations with a different modulus than the one used by the proof system. The modulus of the constraint system is a large prime, and common examples of foreign operations are Boolean operations, field arithmetic, or public-key cryptography operations. We present novel techniques for efficiently embedding such foreign arithmetic in zero-knowledge, including (i) equality of discrete logarithms across different groups; (ii) scalar multiplication without requiring elliptic curve operations; (iii) proving knowledge of an AES encryption. Our approach combines rejection sampling, sigma protocols, and lookup protocols. We implement and provide concrete benchmarks for our protocols.
The proliferation of numerous portable mobile devices has made mobile crowd-sensing (MCS) systems a promising new trend. Traditional MCS systems typically outsource sensing tasks to the data aggregator (e.g., cloud server). They collect and analyze the provided sensing data through an appropriate truth discovery (TD) method to identify valuable data sets. However, existing privacy-preserving MCS systems lack transparency, enabling data aggregators to deviate from the specified protocols and allowing malicious users to provide false or invalid sensing data, thereby contaminating the resulting data sets. The lack of transparency and public verifiability in MCS systems undermines widespread adoption by preventing data requesters from confidently verifying data integrity and accuracy. To address this issue, we propose a transparent and privacy-preserving mobile crowd-sensing system with truth discovery (TP-MCS) constructed using zero-knowledge proof (ZKP) and the Merkle commitment tree. This scheme enables data requesters to effectively verify the correctness of the truth discovery service while ensuring data privacy. Furthermore, theoretical analysis and extensive experiments demonstrate that this scheme is secure and efficient.
The integration of Artificial Intelligence (AI) and Blockchain technology has opened new possibilities in secure distributed systems, addressing several inherent challenges in cybersecurity, trust management, and data privacy. This article explores the synergies between AI and Blockchain, focusing on how they can complement each other in creating secure, transparent, and efficient distributed systems. AI's capabilities in predictive analytics, machine learning, and decision-making combined with Blockchain's decentralized, immutable ledger offer enhanced security and operational efficiency for applications ranging from financial transactions to healthcare and supply chain management. Through a comprehensive analysis of recent advancements and case studies, we highlight the practical implications of these technologies in fostering secure distributed systems and provide a roadmap for their future integration.
The protection of personal privacy has become a paramount issue in the field of data science, with its significance continuously rising. Differential privacy technology has garnered significant attention for its effectiveness in preserving individual privacy. However, the implementation of differential privacy relies on a degree of trust in the entities or individuals executing the algorithms. This paper proposes an innovative solution: a verifiable differential privacy mechanism based on zero-knowledge proofs. This approach integrates differential privacy with zero-knowledge proof technology to not only verify the correctness of the differential privacy techniques but also enhance the transparency and reliability of the algorithms. Additionally, we have designed a publicly verifiable data release scheme that integrates commitment mechanisms and range proofs, ensuring that the range of published data noise does not exceed predetermined thresholds, thereby ensuring the utility of the data. Compared to other verifiable differential privacy solutions, our approach is unique in that it does not rely on the number of participants but is solely dependent on the precision of the data. This means that our computational cost will not increase with the addition of more participants. Finally, we conducted a performance evaluation of the solution, which only took 700ms to complete a single verification. On average, there was a 6% reduction in expectation and a 40% reduction in variance, demonstrating the enhancement of its data utility and the feasibility and effectiveness in practical applications.
Multi-Party Computation (MPC), as a distributed computing paradigm, is considered to be a potential solution for providing privacy-preserving for applications following the client-server model. However, traditional MPC solutions cannot satisfy the publicly verifiable requirement of the client-server model. In this paper, we propose a blockchain-based verifiable MPC solution using Pedersen's threshold secret sharing and Lifted ElGamal encryption. We first build a data distribution method using Pedersen's threshold secret sharing and symmetric encryption to protect the privacy of inputs while ensuring robustness. Then, we propose a result processing algorithm using Lifted ElGamal encryption to safeguard the privacy of the outputs. Finally, we employ non-interactive zero-knowledge proof and Pedersen commitment to publicly verify the correctness of the encrypted outputs in the smart contract, enabling the detection of malicious parties. Theoretical analysis indicates that the proposed method can publicly verify the correctness of outputs without revealing plain-text inputs and outputs, which satisfy the privacy-preserving requirements of the client-server model. Experimental evaluations have demonstrated that our proposed approach is efficient regarding computation overhead, communication overhead, and response time in the output verification phase while achieving stronger privacy and robustness.
Farhana Javed, Engin Zeydan, Josep Mangues‐Bafalluy, Kapal Dev · 5 authors
As edge computing gains prominence in Internet of Things (IoTs), smart cities, and autonomous systems, the demand for real-time machine intelligence with low latency and model reliability continues to grow. Federated Learning (FL) addresses these needs by enabling distributed model training without centralizing user data, yet it remains reliant on centralized servers and lacks built-in mechanisms for transparency and trust. Blockchain, a type of Distributed Ledger Technologies (DLTs) can fill this gap by introducing immutability, decentralized coordination, and verifiability into FL workflows. This article presents current standardization efforts from 3GPP, ETSI, ITU-T, IEEE, and O-RAN that steer the integration of FL and blockchain in IoT ecosystems. We then propose a blockchain-based FL framework that replaces the centralized aggregator, incorporates reputation monitoring of IoT devices, and minimizes overhead via selective on-chain storage of model updates. We validate our approach with IOTA Tangle, demonstrating stable throughput and block confirmations, even under increasing FL workloads. Finally, we discuss architectural considerations and future directions for embedding trustworthy and resource-efficient FL in emerging 6G networks and vertical IoT applications. Our results underscore the potential of DLT-enhanced FL to meet stringent trust and energy requirements of next-generation IoT deployments.
Sang-Hyeon Park, Jeonghyuk Lee, Seunghwa Lee, Jung Hyun Chun · 8 authors
Merging Internet (web2) identities with blockchain (web3) identities is increasingly important for enhancing user experience and ensuring regulatory compliance. However, conventional solutions that map web2 identities to web3 accounts often lead to privacy concerns and fragmented identifiers across networks. To address these challenges, we propose a new identity scheme named Address Abstraction (AA), which redefines blockchain address and signing systems while preserving key properties: uniqueness, immutability, and privacy-preservation. This approach eliminates the limitations of chain-specific identity systems, enabling users to interact with multiple blockchains using their web2 certificates and unified identifiers. This chain-agnostic identifier also promotes cross-chain compatibility. We further present Zero-Knowledge Address Abstraction (zkAA), an implementation of AA that uses zero-knowledge proofs to uphold AA's core properties. Additionally, a proof aggregation technique combines multiple proofs into one, achieving approximately 5.5 times gas cost savings during verification in real-world scenarios. As of August 2024, zkAA with proof aggregation incurs an additional cost of only $0.66 per transaction on Ethereum.
Background: Even while traditional Raft is effective at leader election and log replication, it is not appropriate for sensitive applications like supply chains, financial systems, or healthcare because it lacks built-in privacy safeguards. Materials and Methods: A privacy-preserving Raft consensus method is proposed to solve the privacy issues that occur when private information is transferred between nodes in a distributed system such as a blockchain. Raft itself, by default, does not provide any steps toward ensuring data confidentiality during consensus. By employing privacy-preserving cryptographic techniques like homomorphic encryption and zero-knowledge proofs, nodes can reach consensus while keeping sensitive data private. Results: Traditional Raft performs much better in scenarios where performance matters, while Privacy-Perving Raft works better in a sensitive application to privacy (the average of write throughput is 5% lower than that of traditional Raft) and CPU is 40-60%. Conclusion: Based on the gained privacy by some computational costs, it will be valid to draw the conclusion that this works for privacy-sensitive applications within decentralized systems with these performance and security analyses.
In response to the issues of high transaction transparency and regulatory difficulties in blockchain account-model transactions, this paper presents a supervised blockchain anonymous transaction model based on certificateless signcryption aimed at ensuring secure blockchain transactions while minimizing both computational and communication overhead. During the transaction process, this approach utilizes certificateless public key signcryption without bilinear pairs to generate anonymous user identities, achieving strong anonymity of user identities and confidentiality of transaction amounts. It employs the Paillier homomorphic encryption algorithm to update transaction amounts and uses the FO commitment-based zero-knowledge proof scheme to validate transaction legality. Additionally, adopting a publicly verifiable secret threshold sharing scheme for hierarchical regulatory authority reduces the security risk of a single regulator storing the regulatory key. This model not only meets the privacy and timely update requirements of account-based blockchain transactions but also effectively regulates abnormal transactions. Rigorous security analysis and proofs demonstrate that this model possesses excellent anonymity, traceability, forward security, and backward security. When compared to similar schemes, the computational cost is reduced by at least 33.18%, effectively fulfilling the requirements for security.
Blockchain technology, a decentralized and immutable ledger, has transformed identity and access management (IAM) by enhancing security, privacy, and trust in digital ecosystems. Ensuring safe authentication and data integrity is made possible by its integration with sophisticated cryptographic techniques like zero-knowledge proofs (ZKPs) and public- key infrastructure (PKI). Other methods include verifiable credentials (VCs) and decentralized identifiers (DIDs). This paper provides a comprehensive analysis of blockchain-based IAM systems, comparing leading blockchain platforms, including Ethereum, Hyperledger Indy, IOTA, and IoTeX, in identity management. The role of blockchain in mitigating identity-related threats, such as identity theft and unauthorized access, is explored through decentralization, immutability, and smart contract automation. Additionally, key security enhancements, including cryptographic mechanisms that strengthen decentralized identity solutions and privacy-preserving authentication, are examined. The potential of blockchain to establish a self-sovereign identity framework that fosters trust, scalability, and security in digital identity ecosystems is highlighted, paving the way for the next generation of identity management solutions.
Aishwarya Parab, P. Pradhan, Yogesh Simmhan, Arnab K. Paul
The increasing availability of data from diverse sources, including trusted entities such as governments, as well as untrusted crowd-sourced contributors, demands a secure and trustworthy environment for storage and retrieval. Blockchain, as a distributed and immutable ledger, offers a promising solution to address these challenges. This short paper studies the feasibility of a blockchain-based framework for secure data storage and retrieval across trusted and untrusted sources, focusing on provenance, storage mechanisms, and smart contract security. Through initial experiments using Hyper Ledger Fabric (HLF), we evaluate the storage efficiency, scalability, and feasibility of the proposed approach. This study serves as a motivation for future research to develop a comprehensive blockchain-based storage and retrieval framework.
With the emergence of illegal behaviors such as money laundering and extortion, the regulation of privacy-preserving cryptocurrency has become increasingly important. However, existing regulated privacy-preserving cryptocurrencies usually rely on a single regulator, which seriously threatens users’ privacy once the regulator is corrupt. To address this issue, we propose a linkable group signature against malicious regulators (ALGS) for regulated privacy-preserving cryptocurrencies. Specifically, a set of regulators work together to regulate users’ behavior during cryptocurrencies transactions. Even if a certain number of regulators are corrupted, our scheme still ensures the identity security of a legal user. Meanwhile, our scheme can prevent double-spending during cryptocurrency transactions. We first propose the model of ALGS and define its security properties. Then, we present a concrete construction of ALGS, which provides CCA-2 anonymity, traceability, non-frameability, and linkability. We finally evaluate our ALGS scheme and report its advantages by comparing other schemes. The implementation result shows that the runtime of our signature algorithm is reduced by 17% compared to Emura et al. (2017) and 49% compared to KSS19 (Krenn et al. 2019), while the verification time is reduced by 31% compared to Emura et al. and 47% compared to KSS19.
Muhammad Firdaus, Harashta Tatimma Larasati, Kyung Hyune-Rhee
Healthcare data is often fragmented across various institutions due to its highly sensitive and private nature. In this sense, hospitals and clinics maintain electronic health records (EHRs) independently; hence, valuable data is siloed within individual organizations, preventing comprehensive analysis that could benefit from diverse data sources. Federated learning (FL) addresses these challenges by enabling the training of a shared global model using data distributed across multiple institutions without moving the data from its source. By leveraging FL, healthcare institutions can combine their data assets to improve predictive analytics, personalized medicine, and overall healthcare outcomes, ultimately benefiting patients and the healthcare system. However, the current FL model with a central server presents several challenges within healthcare, including the risk of malicious attacks, regulatory compliance, and privacy vulnerabilities. To overcome these issues, this paper introduces the FL framework with blockchain and homomorphic encryption (HE). Our framework aims to minimize the role of the central server, enable collaborative model training across healthcare organizations, and enhance data security and privacy. In this sense, blockchain ensures the integrity and transparency of the process, while homomorphic encryption ensures that the data remains private. This framework can potentially enable institutions to enrich medical knowledge while securely keeping patient data collaboratively and facilitating healthcare analytics in practical settings.
A Decentralized Identifier (DID) empowers an entity to prove control over a unique and self-issued identifier without relying on any identity provider. The public key material for the proof is encoded into an associated DID document (DDO). This is preferable shared via a distributed ledger because it guarantees algorithmically that everyone has access to the latest state of any tamper-proof DDO but only the entities in control of a DID are able to update theirs. Yet, it is possible to grant deputies the authority to update the DDO on behalf of the DID owner. However, the DID specification leaves largely open on how authorizations over a DDO are managed and enforced among multiple deputies. This article investigates what it means to govern a DID and discusses various forms of how a DID can be controlled by potentially more than one entity. It also presents a prototype of a DID-conform identifier management system where a selected set of governance policies are deployed as Smart Contracts. The article highlights the critical role of governance for the trustworthy and flexible deployment of ledger-anchored DIDs across various domains.
Machine Learning (ML) in distributed environments increasingly deals with sensitive data (like healthcare or financial records) that cannot be centrally stored or processed due to privacy concerns. Federated Learning (FL) addresses this by enabling model training across decentralized devices, but faces significant challenges including system reliability, node failures, and trust issues among participants. Traditional FL approaches often rely on centralized coordinators, creating single points of failure and potential security vulnerabilities. This paper presents a novel approach to FL that leverages smart contracts, blockchain, and decentralized storage to enhance the traceability and reliability of the learning process. Our proposed system architecture is fully decentralized, eliminating single points of failure and promoting cooperation through a rewarding mechanism. Unlike previous approaches that neglect node fault tolerance, we introduce a smart contract based scheme for managing node failures and electing the aggregator node. The presence of the smart contract, executed on a decentralized permissioned blockchain, provides reliability guarantees and eliminates the need for costly distributed algorithms in terms of message exchange. An experimental study is conducted to evaluate various aspects of the FL system. We present results related to the accuracy and effectiveness of the FL system on ML models. We also examine the performance related to the distribution of the weights of the ML model based on the use of IPFS. Furthermore, we analyze the performance of the smart contract in terms of gas consumption. Lastly, we investigate the impact of failures combined with incentive policies and aggregator election algorithms on the FL system. Our findings demonstrate the viability of the proposed approach, paving the way for more robust, reliable, and efficient FL systems.
Federated Learning (FL) has emerged as a promising paradigm in distributed machine learning, enabling collaborative model training while preserving data privacy. However, despite its many advantages, FL still contends with significant challenges -- most notably regarding security and trust. Zero-Knowledge Proofs (ZKPs) offer a potential solution by establishing trust and enhancing system integrity throughout the FL process. Although several studies have explored ZKP-based FL (ZK-FL), a systematic framework and comprehensive analysis are still lacking. This article makes two key contributions. First, we propose a structured ZK-FL framework that categorizes and analyzes the technical roles of ZKPs across various FL stages and tasks. Second, we introduce a novel algorithm, Verifiable Client Selection FL (Veri-CS-FL), which employs ZKPs to refine the client selection process. In Veri-CS-FL, participating clients generate verifiable proofs for the performance metrics of their local models and submit these concise proofs to the server for efficient verification. The server then selects clients with high-quality local models for uploading, subsequently aggregating the contributions from these selected clients. By integrating ZKPs, Veri-CS-FL not only ensures the accuracy of performance metrics but also fortifies trust among participants while enhancing the overall efficiency and security of FL systems.