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Department of Computer Science, City University of Hong Kong, Hong Kong, China
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By intelligently weighting prototypes based on their uncertainty, CAFedCL sidesteps the "prototype bias loop" that plagues federated learning with imbalanced data, leading to significant gains in both accuracy and fairness.
