Robust and Verifiable Privacy Federated Learning

Federated learning (FL) safeguards user privacy by uploading gradients instead of raw data. However, inference attacks can reconstruct raw data using gradients uploaded by users in FL. To mitigate this issue, researchers have combined privacy computing techniques with FL. However, these techniques may not ensure the Byzantine robustness of aggregation or the integrity of the aggregated outcomes. Most current robust privacy FL methods assess differences between gradients and benchmarks in the direction, allowing adversaries to poison the aggregation against the magnitude. Furthermore, these methods cannot ensure the integrity of the aggregation results. To overcome these challenges, this study proposes a novel algorithm, robust and verifiable privacy federated learning (RVPFL), which can more effectively eliminate the poisoning attack of the opponent by measuring the direction and magnitude of the gradient in the ciphertext state. The proposed algorithm guarantees the integrity of server aggregation results while safeguarding user privacy. In this study, comprehensive theoretical analysis and experimental validation of RVPFL are conducted to demonstrate its superiority. The proposed RVPFL algorithm solves the Byzantine robustness problem of aggregation and the integrity problem of aggregation results, which helps to research and develop more robust and effective privacy-preserving federal learning techniques.