Jul 2026· International Journal of Latest Technology in Engineering Management & Applied Science· Vol 15, pp. 2541-2550· 0 citations
TL;DR
BFL-Guard is presented, 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).
Abstract
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.
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.
Raman Dubey, A. Jain, Richa Sharma· International Conference on...· 0 citations
Federated learning (FL) enables collaborative model training without centralizing raw training records, but it does not inherently provide verifiable model provenance, enforceable fairness policies, or auditable control over aggregation. This paper presents FairAI, a blockchain- and IPFS-enabled framework that treats each local model as a governed artifact linked to performance and group-fairness metrics, content identifiers, manifests, Groth16 evidence, and smart-contract decisions. Only models approved on-chain and subsequently retrieved and validated through their registered CIDs are eligible for aggregation. The primary real-data evaluation used the Adult and COMPAS datasets under IID and joint label/protected-group non-IID partitions, with ten paired seeds comparing standard FedAvg, post hoc fairness assessment, a pre-aggregation fairness policy gate, and FairFed. Under heterogeneous Adult data, the policy gate reduced the demographic-parity gap from 0.0273 to 0.0127, while accuracy decreased from 0.7740 to 0.7629. Under heterogeneous COMPAS data, the equalized-odds gap decreased from 0.2262 to 0.1226, while accuracy decreased from 0.6495 to 0.5809; the paired accuracy and equalized odds differences remained significant after Holm correction, with adjusted p-values of 0.0318 and 0.0491, respectively. Additional bounded experiments evaluated a small multilayer perceptron, policy threshold sensitivity, logical-client scaling, poisoning, coordinate-wise median aggregation, two native Kubo/IPFS peers, V2 Groth16 verification, and smart-contract overhead. Thirty valid V2 proofs were accepted, six inconsistent cases were rejected, and direct Solidity verification consumed 348,811 gas per measured transaction. A full-path false-metric experiment showed that the proof verifies threshold compliance and artifact binding for supplied values, but does not establish their correct derivation from private data. When an approved artifact became unavailable, FairAI cancelled the round before aggregation and published no global model.
A trust-based federated learning framework in which a smart contract enabled by blockchain oversees client registration, model update logging, hash-based integrity verification, trust score calculation, malicious node penalization, aggregation approval, and decentralised audit logging is proposed.
Shankar Thalla· International Journal of Lat...· 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
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
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
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