Blockchain-Based Fairness-Enhanced Federated Learning Scheme Against Data Poisoning Attack

Abstract

The federated learning technology provides a new method for data integration, which realizes sharing of a global model and prevent the leakage of user’s original data information. In order to resist data poisoning attack from some participants, ensure reliability and accuracy of the global model, and ensure fairness of the aggregation process in federated learning, we propose a blockchain-based fairness enhanced federated learning scheme. The accuracy of global model and fairness of the aggregation process is guaranteed by an adaptive aggregation algorithm which can defense data poisoning attack. The reliability of federated learning process is ensured by recording the entire process of the model training on the blockchain and using digital signatures. The privacy of each participant of federated learning is protected by public key encryption combined with the use of random numbers. Theoretical analysis and experiments show that the scheme can protect privacy of each participant, mitigate data poisoning attack and ensure the reliability and fairness of the entire federated learning process.