Blockchain for Securing Federated Learning Systems: Enhancing Privacy and Trust
Tarun Kumar Vashishth, Vikas Kumar Sharma, Bhupendra Kumar, Kewal Krishan Sharma, Sachin Chaudhary, Rajneesh Panwar · 2024
Federated learning has emerged as a promising paradigm for training machine learning models across distributed devices while preserving data privacy. However, the decentralized nature of federated learning systems poses significant challenges in terms of security, privacy, and trust. This paper presents a novel approach to address these challenges by integrating blockchain technology into federated learning systems. The proposed blockchain-based framework enhances the security and privacy aspects of federated learning by introducing a distributed and tamper-resistant ledger to record model updates, data contributions, and consensus mechanisms. Through the use of smart contracts, the framework facilitates trust less coordination among participants, eliminating the need for a central authority and minimizing the risk of data manipulation or unauthorized access. Furthermore, the incorporation of blockchain technology empowers participants to maintain control over their data, granting them the ability to provide selective access to specific model parameters while keeping other sensitive information private. The immutability of blockchain ensures transparency and auditability, enhancing the accountability and verifiability of the federated learning process. In addition to strengthening security and privacy, the proposed blockchain-based federated learning system achieves greater scalability and robustness by mitigating issues related to data distribution and device heterogeneity. The distributed nature of the blockchain network allows for efficient communication and coordination between nodes, reducing the computational burden on individual devices and optimizing the overall training process. To evaluate the effectiveness of the blockchain-enhanced federated learning system, extensive experiments are conducted on diverse datasets and network configurations. The results demonstrate significant improvements in model convergence, accuracy, and resistance against adversarial attacks compared to conventional federated learning setups. In conclusion, this paper introduces a pioneering approach that leverages blockchain technology to enhance security, privacy, and trust in federated learning systems. By combining the strengths of federated learning and blockchain, the proposed framework paves the way for the widespread adoption of privacy-preserving machine learning solutions in various industries, including healthcare, finance, and Internet of Things (IoT) applications.