FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
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 ...
| Authors | Ruiyang Wang, Rong Pan, Zhengan Yao |
|---|---|
| Primary category | cs.CR |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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