Privacy-Preserving Federated Primal-Dual Learning for Non-convex and Non-smooth Problems with Bidirectional Model Sparsification
The authors propose two novel privacy-preserving federated primal-dual learning algorithms, DP-FedPDM and BSDP-FedPDM, to efficiently solve non-convex and non-smooth federated learning problems while considering communication efficiency and privacy protection.