In cross-silo federated learning, combining client bids, Shapley-based reputation, and budget-constrained 0-1 selection improves global model accuracy over random selection under label noise and low-bid interference.
Clustered sampling: Low- variance and improved representativity for clients selection in federated learning
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Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection
In cross-silo federated learning, combining client bids, Shapley-based reputation, and budget-constrained 0-1 selection improves global model accuracy over random selection under label noise and low-bid interference.