Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 640-644· 0 citations· 22 references
Abstract
Federated Learning (FL) enables distributed machine learning without sharing raw data, but its reliance on a central aggregation server introduces critical vulnerabilities gradient inversion attacks, Byzantine poisoning, free-riding by rational participants, and single-point-of-failure risk. Blockchain has been proposed as a structural remedy, giving rise to the field of Blockchain-Enabled Federated Learning (BEFL). reviews exactly eight representative peer-reviewed BEFL systems published selected to cover four core challenge dimensions: privacy, security, scalability, and incentive design. compare each system across accuracy under data heterogeneity, formal privacy guarantees, Byzantine robustness, throughput, and communication efficiency. find that every reviewed system excels on one or two dimensions while underperforming on others, and that no single published system simultaneously resolves all four challenges. Based on this review identify four fundamental research gaps and conclude with a structured research agenda. This study also reveals that the base FL optimizer contributes more to model accuracy than any blockchain or privacy mechanism a finding with significant design implications.
Federated Learning (FL) enables collaborative model training across decentralized participants without sharing raw data. However, existing FL systems remain vulnerable to Byzantine attacks and suffer from a lack of accountability, verifiability, and economic incentives for honest participation. We present BFL-Guard, a novel blockchain-orchestrated federated learning framework integrating: (i) zk-SNARK-based zero-knowledge gradient proofs, (ii) an on-chain Byzantine-tolerant aggregation smart contract, and (iii) a tokenized incentive protocol (FedToken). BFL-Guard stores model checkpoints as IPFS hashes anchored on Ethereum, ensuring tamper-evident auditability. Experiments on CIFAR-10 and Shakespeare benchmarks demonstrate 95.2% and 87.6% accuracy in IID and Non-IID settings, surpassing all baselines while converging 12.4% faster even under 30% Byzantine injection.
Kumaresan S, Thirumal L, E. V et al.· International Journal of Lat...· 0 citations
A blockchain-based federated learning framework based on quality auditing, fairness deviation, and Sybil-resistant similarity (FedQFS) is proposed, which achieves over 95.5% accuracy on the MNIST dataset and maintains strong robustness under Sybil attacks scenarios.
Tian Fang· Poster Volume 0008 The 2026...· 0 citations
This research reveals that federated learning by blockchain is a robust and scalable platform to enable privacy-preserving artificial intelligence in healthcare, finance, IoT, smart city, and industrial applications.
Arthi D, R. Anand, Palaniappan Sambandam et al.· International journal of com...· 0 citations
Blockchain technology has emerged as a transformative tool for secure data sharing across decentralised systems, particularly in finance, healthcare, and governance. However, despite its promise, the widespread adoption of blockchain platforms remains constrained by unresolved security threats and architecture-specific vulnerabilities. This paper presents a systematic literature review (SLR) that critically evaluates the security risks and countermeasures associated with blockchain-based data sharing models. The review focuses on three widely referenced platforms, Ethereum, Hyperledger Fabric, and MedRec, chosen for their relevance to public, permissioned, and healthcare-oriented blockchain deployments, respectively. The review analyzed 30 peer-reviewed publications from 2018 to 2025 sourced from IEEE Xplore, SpringerLink, ScienceDirect, and other digital libraries. Empirical insights from the reviewed literature indicate that Sybil attacks remain prevalent on public blockchains, although adaptive Proof-of-Stake protocols are reported to reduce their success rate considerably. Front-running and Miner Extractable Value–related behaviors are frequently observed in Ethereum-based decentralised finance ecosystems, often resulting in significant financial losses. Unauthorised access persists as a major concern, particularly for software wallets, which are commonly exposed to phishing and malware attacks. The findings underscore unique trade-offs across platforms: Ethereum supports transparency but is prone to transaction manipulation; Hyperledger ensures strong access control yet faces insider threat challenges; and MedRec enhances patient privacy but lacks robust mobile integration. This study provides a structured synthesis of existing threats, platform-level responses, and design trade-offs, offering guidance for stakeholders aiming to strengthen security in blockchain-based data-sharing infrastructures.
Godwin Mandinyenya, Vusumuzi Malele· International Journal of Inf...· 0 citations
In recent years, the rapid growth of distributed artificial intelligence (AI) and blockchain technology has led to new opportunities for building secure, transparent, and privacy-preserving learning systems. Federated Learning (FL) enables multiple users or organizations to collaboratively train a global AI model without sharing their private data, while Blockchain provides immutability, traceability, and decentralized trust. However, most existing blockchain-based FL systems are limited to a single network, lacking interoperability and scalability across multiple chains. This review paper explores the emerging concept of Cross-Chain Federated Learning (CCFL), which integrates federated learning with cross-chain blockchain communication to achieve secure and interoperable decentralized AI. The paper discusses existing research works, current architectures, algorithms, and cross-chain mechanisms, identifying key challenges such as model verification, communication overhead, and data integrity. Furthermore, it highlights how the proposed framework addresses these challenges by using smart contracts, cryptographic hashing, and relayer-based synchronization.The study concludes that integrating FL with cross-chain blockchain technology can significantly enhance privacy, security, and collaboration among diverse AI systems, paving the way for next-generation decentralized intelligence.
Keywords— Federated Learning, Blockchain, Cross-Chain Communication, Decentralized AI, Data Privacy, Smart Contracts, Secure Aggregation, Interoperability
Vikrant Thombare, Mahendra Sawane· International Journal of Cre...· 0 citations
The convergence of Federated Learning (FL), Blockchain, and Edge Computing presents a transformative paradigm for decentralized, secure, and privacy-preserving machine learning at the network edge. FL enables collaborative model training without centralizing data, while Blockchain provides immutable and transparent mechanisms for trust, accountability, and coordination among distributed edge nodes. Edge computing further enhances this ecosystem by offering low-latency computation near data sources. Despite the promise of this triad, significant challenges persist in terms of scalability, energy efficiency, consensus mechanisms, data and model security, and system heterogeneity. This paper provides a comprehensive survey of the intersection of FL, Blockchain, and Edge Computing, analyzing key opportunities, current solutions, and open challenges. We also discuss architectural frameworks, real-world applications, and future research directions.
Lakshmi Narayanan, A. Turing· International Journal of Dat...· 0 citations
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