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This paper introduces SCOPE-FL, a hierarchical federated learning framework that uses the Top Trading Cycle algorithm to ensure strategy-proofness and Pareto efficiency in client selection, with reward distribution via Shapley value approximation and blockchain-based execution.
This paper proposes a quantum annealing approach that reformulates client selection in federated learning as a QUBO problem to defend against Byzantine attacks, showing improved detection accuracy over classical MultiKrum on sophisticated attacks, especially when combined with a MultiSignal ensemble.