Efficient and Privacy-Preserving Federated Learning with Irregular Users

Abstract

Federated learning (FL) enables multiple users to learn a global predictive model by exchanging local updates without disclosing their private datasets. To further protect local updates, several privacy-preserving schemes are proposed and applied in FL. However, a fundamental issue is that irregular users in FL holding low quality updates could decrease the convergence rate, and even worse, damage the model&#x2019;s usability. While a few works recently explore unified solutions to mitigate the issues of privacy and irregular users meanwhile, the existing methods are still insufficient in terms of accuracy and efficiency. The reasons are two major limitations: inefficiency caused by complex cryptographic algorithms and poor model usability due to ineffective removing strategies for irregular users. To approach the above problems, we propose SAP-IU, a new and efficient federated learning scheme, which achieves irregular users removing and privacy protection at the same time. Specifically, we first design a novel removing algorithm for irregular users called Trust<inf>IU</inf> that calculates the weight of each user via the cosine metric. This ensures that the global model is mainly derived from the contributions of high-quality data. We further devise a secure weighted aggregation protocol for Trust<inf>IU</inf> to protect users&#x2019; sensitive information including local updates and data quality. Besides, our scheme is robust to users dropping out during the whole training process. Moreover, extensive experiments show that SAP-IU has a better performance than prior works in terms of training accuracy and efficiency.