Instruction: Propose methods to measure and enhance fairness in Federated Learning models across diverse clients.
Context: Candidates must discuss approaches to ensure fairness in model outcomes, addressing potential biases in Federated Learning.
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I would quantify fairness by measuring performance and error disparities across meaningful client groups, not just across individual examples. In federated learning, the relevant unit is often the client, institution, region, or device population because the data is...