[2603.27986] FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation

Computer Science > Cryptography and Security arXiv:2603.27986 (cs) [Submitted on 30 Mar 2026] Title:FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation Authors:Ruiyang Wang, Rong Pan, Zhengan Yao View a PDF of the paper titled FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation, by Ruiyang Wang and 2 other authors View PDF HTML (experimental) Abstract:Federated learning (FL) enables distributed clients to collaboratively train a global model using local private data. Nevertheless, recent studies show that conventional FL algorithms still exhibit deficiencies in privacy protection, and the server lacks a reliable and stable aggregation rule for updating the global model. This situation creates opportunities for adversaries: on the one hand, they may eavesdrop on uploaded gradients or model parameters, potentially leaking benign clients' private data; on the other hand, they may compromise clients to launch poisoning attacks that corrupt the global model. To balance accuracy and security, we propose FedFG, a robust FL framework based on flow-matching generation that simultaneously preserves client privacy and resists sophisticated poisoning attacks. On the client side, each local network is decoupled into a private feature extractor and a public classifier. Each client is further equipped with a flow-matching generator that replaces the extractor when interacting with the server, thereby protecting private fea...