Sep 2026· IEEE Transactions on Smart Grid· Vol 17, pp. 4630-4643· 1 citation· 35 references
Abstract
Malicious clients participating in data collection and interaction may launch attacks such as model and data poisoning to degrade the performance of the global model and conceal their electricity theft behaviors. Although existing studies have introduced blockchain technology to achieve decentralization, they still suffer from limited pre-aggregation validation dimensions. To address these issues, this paper proposes a blockchain-based federated learning approach with dual-verification (BFL-DV) for electricity theft detection. In the pre-aggregation stage, a multi-metric reputation-based consensus committee verification strategy is designed, which effectively mitigates the impact of malicious participants. In the post-aggregation stage, a dynamic threshold-based blockchain verification strategy is developed to counter security risks during the transmission process, which can refuse malicious global updates adaptively. Experimental results demonstrate that BFL-DV can accurately reduce the impact of all malicious clients under the data poisoning attack. Notably, across various proportions of malicious clients, the proposed framework achieves an average AUC improvement of 32.68% compared with SOTA methods, demonstrating its consistent performance advantage.
Federated Learning (FL) integrated with blockchain technology effectively mitigates the risks of single points of failure and malicious server behavior inherent in traditional federated learning architectures that rely on central servers. However, in practical implementation, the system still faces significant challenges in ensuring security and fairness. Malicious clients may upload low-quality or biased local models to disrupt global convergence, while Sybil nodes can forge multiple identities to manipulate aggregation, leading to severe performance degradation. To address these issues, this paper pro-poses a blockchain-based federated learning framework based on quality auditing, fairness deviation, and Sybil-resistant similarity (FedQFS). First, a quality auditing mechanism is designed to evaluate and filter local model updates through multi-dimensional metrics, effectively mitigating global ac-curacy degradation caused by malicious updates. Second, a Sybil-resilient identification mechanism is introduced, which leverages parameter similarity analysis to accurately detect forged identities, thereby enhancing the system's resistance against Sybil attacks. Finally, a fairness deviation quantification mechanism is incorporated to measure parameter distribution disparities and adaptively assign reasonable aggregation weights to benign clients with limited data, ensuring fairness in global model updates. Experimental results show that the proposed framework achieves over 95.5% accuracy on the MNIST dataset and maintains strong robustness under Sybil attacks scenarios. When four label-flipping attackers are present, its attack success rate de-creases by 18.4% compared with mainstream aggregation algorithms, validating the proposed method’s efficiency and security in complex distributed environments.
Tian Fang· Poster Volume 0008 The 2026...· 0 citations
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.
Arthi D, R Anand, Palaniappan Sambandam et al.· International journal of com...· 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
During humanitarian crises, natural disasters, and public emergencies, social media platforms serve as vital real-time information sources. However, they are also prone to the rapid propagation of misinformation, rumors, and malicious content. Traditional centralized verification systems present severe limitations, including single points of failure, algorithmic bias, censorship concerns, and data privacy issues. To address these vulnerabilities, this paper proposes DeFL-Crisis, a decentralized, blockchain-anchored federated learning framework designed for real-time verification of crisis-related social media content. DeFL-Crisis utilizes federated learning to allow edge client nodes (e.g., local emergency management centers, news agencies, and academic scrapers) to collaboratively train robust misinformation detection models on local data without sharing raw text, thus preserving user privacy. To secure the federated learning process against Byzantine model-poisoning and data-poisoning attacks, we anchor the model aggregation within a consortium blockchain ledger governed by a consensus-driven verification protocol. We implement a Proof-of-Accuracy (PoAC) consensus mechanism and smart-contract-based validation that evaluates client model updates against isolated, validated local validation sets, dynamically computing client reputation scores. Experimental evaluations using simulated benchmarks modeled after CrisisLexT26 and PHEME datasets show that DeFL-Crisis achieves a global verification accuracy of 94.2%, which is within 1.3% of the centralized training upper bound. Under hostile scenarios where 40% of clients perform aggressive model-poisoning attacks, DeFL-Crisis preserves high classification performance (F1-score of 0.88), whereas standard federated learning collapses to an F1-score of 0.45. Furthermore, we demonstrate that blockchain transaction latency is scalable (under 50 seconds for up to 50 active scaling nodes) and smart contract gas costs are highly optimized, indicating the realworld viability of our framework.
S. K, Sachin Singh Butola, S. B et al.· 2026 7th International Confe...· 0 citations
Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem.
Federated Learning (FL) allows multiple data possessors to collaboratively train a shared model without transferring any raw data outside their local administration limits. However, conventional server orchestrated FL is vulnerable to trusted-server reliance, black-box contribution auditability, malicious model updates, and weak accountability. This article proposes 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.
The paper uses a clearly labelled simulated experimental setup involving 16 clients having a non-IID Fashion-MNIST classification task. Three of these 16 clients are malicious and submit either poisoned updates or updates having hash mismatches. The proposed trust-centric blockchain federated learning configuration is compared with traditional federated averaging based on parameters such as model accuracy, verification rate, malicious-update identification, F1-score, transaction cost, and governance traceability. Based on the simulation results, the new framework enhances the global accuracy from 79.6% to 86.8% under the attack setting. Further hash-verification coverage is complete for submitted updates. Likewise, the simulation rejects the three malicious submissions in the simulated round. Lastly, the provenance records become immutable via incurring additional latency of 121.3 ms per update. The paper contributes a technically defined, IEEE-style framework for federated learning, smart-contract trust scoring, model-integrity verification, and decentralized AI governance. The outcomes are not offered as evidence of real-world deployment but as a reproducible academic design and evaluation framework for future empirical real-world deployment.
Shankar Thalla· International Journal of Lat...· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
