Title: FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection
Authors: Ruixuan Liu, Yang Cao, Masatoshi Yoshikawa and Hong Chen
Abstract: As massive data are produced from small gadgets, federated learning on mobile devices has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) has been widely used in federated learning for various machine learning models. To prevent privacy leakages from gradients that are calculated on users' sensitive data, local differential privacy (LDP) has been considered as a privacy guarantee in federated SGD recently. However, the existing solutions have a dimension dependency problem: the injected noise is substantially proportional to the dimension d. In this work, we propose a two-stage framework FedSel for federated SGD under LDP to relieve this problem.
