STAR-FL: Secure Federated Learning with Spatial-Temporal Analysis and Robust Aggregation
Data poisoning attacks pose serious security threats to Federated Learning (FL) systems in Computer Vision. Despite growing research attention, two key challenges remain for existing defense techniques: (1) accurately distinguishing between benign and malicious model updates and (2) effectively mitigating the influence of poisoned model updates during model aggregation. To address these challenges, we propose a novel defense framework against targeted poisoning attacks with Spatial-Temporal Analysis and Robust aggregation for FL (STAR-FL). First, we employ spatial-temporal clustering to identify and remove potentially malicious updates from the FL training process. Second, we adjust the l...
| Authors | Nawrin Tabassum, Yanzhao Wu |
|---|---|
| Primary category | cs.CR |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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