Efficient, Private and Robust Federated Learning
Meng Hao, Hongwei Li, Guowen Xu, Hanxiao Chen, Tianwei Zhang
Abstract
Federated learning (FL) has demonstrated tremendous success in various mission-critical large-scale scenarios. However, such promising distributed learning paradigm is still vulnerable to privacy inference and byzantine attacks. The former aims to infer the privacy of target participants involved in training, while the latter focuses on destroying the integrity of the constructed model. To mitigate the above two issues, a few works recently explored unified solutions by utilizing generic secure computation techniques and common byzantine-robust aggregation rules, but there are two major limitations: 1) they suffer from impracticality due to efficiency bottlenecks, and 2) they are still vulnerable to various types of attacks because of model incomprehensiveness.