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The paper introduces FedeRage, a risk-averse federated learning method that uses conditional value-at-risk to address unknown client participation and heterogeneity, demonstrating enhanced accuracy, fairness, and convergence in experiments.
The paper introduces SWB-DM, a robust aggregation method for federated learning that combines sliced Wasserstein barycenters with delayed momentum caching to handle Byzantine attacks and partial participation effectively.