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FedMaJ: Ordinal Meta-Aggregation for Federated Learning Using Majority-Judgment Voting
Federated learning traditionally relies on a fixed aggregation rule despite shifting client state and wireless dynamics. We present FedMaJ, a framework that dynamically selects aggregation rules via Majority Judgment voting. During voting rounds, clients evaluate candidate models across performance,
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