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CrySyS Lab, Hungary
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Current federated learning contribution metrics are easily manipulated, but this work introduces a new approach that's both private and robust, preventing clients from gaming the system.
Current federated learning evaluation schemes are flawed because a client that boosts accuracy might tank fairness or resilience, demanding a more nuanced approach to valuing contributions.
