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FLEAT: Energy-Accuracy Trade-off in Federated Learning over Heterogeneous Edge Devices
This work proposes FLEAT (Federated Learning Energy and Accuracy Tuning), a framework that jointly optimizes energy efficiency and model accuracy via dynamic local update adaptation and gradient-informed layer-wise pruning, offering a scalable solution for energy-accuracy equilibrium in heterogeneous FL deployments.