Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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.
Abstract
Federated learning helps in collaborative training of AI models without transmitting raw data, and hence it is an exciting approach for privacy-conscious applications. Nonetheless, traditional federated learning still suffers from weaknesses such as susceptibility to attacks from malicious actors, susceptibility to model poisoning, single point of coordination, lack of transparency, and scalability issues. Blockchain technology provides a decentralized method of trusting which can help in enhancing the security, accountability, and integrity of federated learning. This paper presents a comprehensive analysis of blockchain based trustworthy federated learning through the examination of the role played by consensus algorithms, cryptographic security techniques, scalability approaches, and deployment strategies. A systematic literature review was conducted using recently published papers from peer-reviewed sources. The selected papers were analyzed using inclusion and exclusion criteria to reveal technological advancements, deployment problems, and practical applications. Consensus methods, which include Proof of Stake, Practical Byzantine Fault Tolerance, Delegated Proof of Stake, and hybrid versions, illustrate various tradeoffs between security, processing speed, latency, and energy utilization. Encryption algorithms like homomorphic encryption, secure multiparty computation, differential privacy, and digital signatures offer added value to privacy and resilience against malicious activity. The review presents several shortcomings concerning communication overheads, blockchain storage expansion, delays in the consensus process, interoperability, and resource-limited edge computing nodes. Recent innovations, such as lightweight consensus, hierarchical blockchain structures, off-chain storage, and adaptive communication models, reveal high prospects in addressing those shortcomings. 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.
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
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
Collaborative threat intelligence sharing has become essential for defending distributed enterprise and smart city infrastructures against increasingly sophisticated cyber threats. However, organizations remain reluctant to share raw security data due to privacy, regulatory, and trust concerns. Federated Learning (FL) has emerged as a promising solution by enabling collaborative model training without exposing local data. Nevertheless, traditional FL-based intrusion detection frameworks remain vulnerable to model poisoning, Byzantine attacks, and lack transparent accountability mechanisms for cross-organization collaboration. This paper proposes an engineering framework for privacy-preserving federated threat intelligence sharing that integrates trust-aware robust aggregation with blockchain-based integrity anchoring. The proposed architecture introduces a dynamic reputation mechanism that evaluates participant reliability across communication rounds and assigns adaptive aggregation weights to mitigate malicious updates. To enhance transparency and non-repudiation, model update hashes and trust evolution records are anchored on a permissioned blockchain through smart contracts, ensuring immutable auditability without exposing sensitive parameters. The framework is evaluated using a non-IID partition of the ToN-IoT dataset across multiple simulated organizations. Experimental results demonstrate significant robustness improvements under adversarial environments. Under Byzantine attacks with 20% malicious clients, the proposed trust-aware aggregation mechanism achieves approximately 95% Accuracy and 95% F1-macro, compared with approximately 89% obtained using conventional FedAvg. Furthermore, under highly adversarial conditions involving 40% malicious participants, the proposed framework maintains approximately 95% Accuracy and F1-macro, whereas FedAvg degrades to approximately 78% Accuracy and 77% F1-macro. These results confirm the effectiveness of the proposed trust-aware aggregation mechanism in mitigating malicious updates while preserving stable convergence and reliable intrusion detection performance.
Mehdi Houichi, Faouzi Jaidi, Adel Bouhoula· Journal of King Saud Univers...· 0 citations
The proposed framework incorporates shard-based transaction processing, Byzantine fault tolerant (PBFT) consensus, resilient federated aggregation, and AES-GCM-encrypted model updates and demonstrated higher throughput and lower authentication latency under the evaluated workloads.
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 systematic literature review (SLR) investigates the role of blockchain technology in achieving secure decentralized cloud storage. This review synthesizes evidence from 65 peer-reviewed publications (2018–2025) addressing security mechanisms, consensus protocols, privacy-preserving techniques, and scalability challenges in blockchain-enabled decentralized storage systems. Thematic analysis identified four primary focus areas: (1) Security Mechanisms including cryptographic foundations, consensus-based verification, and smart-contract-enabled access control; (2) Consensus Protocols and their performance-security tradeoffs; (3) Privacy-Preserving Techniques addressing the transparency-confidentiality tension; and (4) Technical Challenges including scalability, energy consumption, and regulatory conflicts. Evidence synthesis reveals that while blockchain-based approaches are commonly reported to improve security and auditability compared to centralized cloud storage, methodological limitations and lack of real-world deployments constrain definitive effectiveness claims. This review contributes by: (a) systematically documenting blockchain's technical mechanisms for storage security; (b) identifying critical deployment barriers and their theoretical bases; (c) clarifying the privacy-security tradeoff landscape; and (d) directing future research toward empirical validation and production-ready implementations. The study recommends future research on hybrid blockchain architectures, energy-efficient consensus algorithms, advanced privacy techniques, and more empirical validations to overcome these limitations and promote real-world adoption.
Kamaluddeen Musa Yashi, Isah Muhammad Alhassan, Aminu Adamu Ahmed· Center of Computer Science· 0 citations
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