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	Slide 5. Related works. 1. Federated learning under Non-IID data 2. Noise-Robust Learning in Federated Settings 3. FSANL unifies robust loss design and client drift mitigation in a single-stage FL framework. Further, no clean-client assumption, no label correction, no auxiliary models.

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