SecureAFL: Secure Asynchronous Federated Learning
Federated learning (FL) enables multiple clients to collaboratively train a global machine learning model via a server without sharing their private training data. In traditional FL, the system follows a synchronous approach, where the server waits for model updates from numerous clients before aggregating them to update the global model. However, synchronous FL is hindered by the straggler problem. To address this, the asynchronous FL architecture allows the server to update the global model immediately upon receiving any client's local model update. Despite its advantages, the decentralized nature of asynchronous FL makes it vulnerable to poisoning attacks. Several defenses tailored for...
| Authors | Anjun Gao, Feng Wang, Zhenglin Wan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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