BLOCKCHAIN-BASED PRIVACY-PRESERVING AND SECURE FEDERATED LEARNING FRAMEWORK

Blockchain-Assisted Privacy-Preserving and Robust Federated Learning in Edge Computing

Federated learning (FL) enables distributed clients to collaboratively train machine learning models without sharing raw data, making it well-suited for edge computing environments. However, deploying FL at the edge introduces critical challenges, including potential model poisoning by malicious clients or edge servers, increased privacy risks, and communication bottlenecks. To address these issues, we propose a two-layer blockchain-assisted FL framework that ensures privacy, robustness, and efficiency. In our design, each client uploads an encrypted local update to an edge server for privacy-preserving validation, while a lightweight, sparse upload strategy is adopted to reduce communication overhead. We design a two-layer defense strategy that employs the secure cosine similarity technique and a truth discovery algorithm to resist Byzantine attacks from both malicious clients and edge servers, while a consortium blockchain maintained by edge servers guarantees decentralized and tamper-resistant global model updates. Theoretical analysis and extensive experiments under both IID and non-IID data partitions demonstrate that the proposed scheme effectively defends against targeted and untargeted poisoning attacks and preserves the privacy of local model updates. It also substantially reduces communication overhead. These results indicate that our framework provides a practical and scalable solution for privacy-preserving and robust FL in edge environments.

Blockchain-Enhanced Secure Data Sharing in Financial Institutions: A Federated Learning Framework for Privacy-Preserving Analytics

Purpose The goal of this research is to create a framework for financial system fraud detection that is safe and protects privacy while resolving issues with data sharing, legal restrictions, and cybersecurity threats. To facilitate cooperative model training without exchanging private raw data. Methods A blockchain-enhanced Federated Learning (FL) method is suggested. The technique divides a centralized dataset among several clients to simulate an FL environment. Local training is done using a TabNet deep learning model, and hyperparameter tweaking is done via Bayesian optimization. Input features are normalized and passed through TabNet's sequential feature transformer and attentive transformer, which generate sparse attention masks to select the most discriminative attributes at each decision step; the aggregated decision representations are then classified using a softmax-based prediction layer. Model updates are transmitted using AES-256 encryption to guarantee data security, and their integrity is confirmed by SHA-256 hashing. Blockchain technology provides transparency and tamper resistance by recording model update hashes via Hyperledger Fabric. The integration of AES-256 encryption, SHA-256 integrity verification, and Hyperledger Fabric blockchain enables secure peer-to-peer model transmission, making the proposed framework well suited for decentralized financial environments. Results With an accuracy of 0.9924, precision of 0.9947, recall of 0.9910, and low rates of false positives and false negatives, the findings show excellent performance. Validation accuracy improved consistently across successive federated communication rounds, rising from 0.9887 to 0.9933, confirming stable model convergence, while the framework achieved a ROC-AUC of 0.9980 and PR-AUC of 0.9986. The suggested architecture performs better than conventional models like decision trees and logistic regression. Conclusion The framework is appropriate for practical financial applications since it successfully improves fraud detection while guaranteeing Privacy Preservation, security, and openness.

Enhancing security and integrity in federated learning using blockchain and decentralized storage

This work develops a Blockchain-Enabled Federated Learning (BCFL) framework to address some of the major concerns in federated learning: the use of a central server, the lack of transparency/traceability during the training process and the risk of a malicious participant leaking private information. By utilizing the blockchain as an intermediary to aggregate all the local models trained at each participants location, the BCFL framework ensures the integrity of the aggregated model, provides a transparent record of all transactions and prevents tampering. In addition, this framework utilizes the Interplanetary File System (IPFS) to allow nodes to store their own model parameters locally and provides a decentralized method of managing those parameters. The system uses a lightweight hash-based verification mechanism that allows model updates to be verified using their Content Identifiers (CIDs) on the blockchain, without sending the raw parameters to a central authority. Tests using the MNIST, Fashion-MNIST, and CIFAR-10 datasets show that the BCFL framework improves the accuracy of the Global model in the presence of a 20% label-flipping data poisoning attack by 31.65% to 42.40% compared to standard FL baselines without outlier defense. The integration of blockchain consensus and IPFS storage causes a predictable computational training latency overhead of around 7.5%-8.2%. Yet, the framework achieves baseline predictive accuracy (on average 88.49% on CIFAR-10), which indicates a practical and quantifiable trade-off between computing and decentralized security.

EigenFL: An EigenLayer-Restaked Blockchain Solution for Secure Federated Learning

Federated Learning (FL) enables collaborative model training while preserving data privacy by keeping data decentralized. However, traditional FL architectures suffer from critical trust issues, including vulnerability to model poisoning attacks, a lack of incentives for honest participation, and the inability to verify the quality of submitted model updates. Blockchain-based solutions have been proposed to address these challenges, but they often require participants to bootstrap independent validator networks and lock additional capital, limiting scalability and practical adoption. This paper proposes EigenFL, a novel FL framework secured through blockchain restaking, leveraging EigenLayer to provide cryptoeconomic security and decentralized validation by reusing Ethereum's existing validator infrastructure. Participants are economically incentivized to submit honest updates, while malicious behavior is discouraged through slashing mechanisms. Model updates are validated off-chain by independent operators, and their outcomes are immutably recorded on-chain, ensuring accountability and robustness. EigenFL demonstrates how blockchain restaking can transform FL from a trust-based paradigm into a cryptoeconomically secured framework, enabling verifiable and decentralized training.

Privacy Preservation and Attack Resistance in Blockchain-Enhanced Federated Learning for Medical Data

Using federated learning (FL) in health care applications, the teams will be able to collaborate to predict the health of patients without exchanging any personally identifiable information. Regardless of this benefit, FL is still not widely used since it is susceptible to serious privacy and security risks like membership inference and model poisoning attacks. To address these concerns, this paper proposes a blockchain-enhanced FL model that involves safe aggregation and audit-trail immutability to thwart privacy leakage and manipulation of medical data analysis by opponents. Random Forest and LightGBM classifiers are trained on fake healthcare datasets in various scenarios of attack. Some of the security measures that are used to assess the system include attack success rate, cost of communication, differential privacy (DP) epsilon values and blockchain-based tamper detection delay. The proposed approach can achieve measurable privacy leaks reduction and a 40-percent lower success rate in membership inference attacks, as demonstrated in experiments. Also, blockchain audit trails can provide real-time resilience against tampering of data. According to these findings, blockchain-enhanced FL appears to be a promising framework of medical AI systems capable of ensuring the patient data safety and privacy.

A Secure Federated Learning and Blockchain Framework for E-Health Threat Detection