A verifiable and privacy-preserving blockchain-based federated learning approach

Federated Learning (FL) is a promising approach to enabling machine learning on decentralized data. It allows multiple clients to train a global model without transferring their data to a central server. However, traditional federated learning suffers from privacy and security problems due to the potential leakage of sensitive information. Existing consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS) etc., are not scalable and efficient for permissioned blockchain networks. In this paper, we propose a blockchain-based federated learning approach using the Proof of Authority (PoA) consensus algorithm to address these issues. The proposed framework leverages the immutability and transparency of blockchain to ensure the integrity and privacy of the data during the federated learning process. We evaluate the proposed blockchain-based FL approach on a simulated dataset, and the results show that it achieves a higher level of accuracy, efficiency, privacy and security compared to existing approaches. We also compare the PoA consensus algorithm with other consensus algorithms. The proposed approach, Blockchain-based Federated Learning (BC-FL) is designed to be more communication efficient, scalable, and secure than existing approaches in blockchain-based FL systems.