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Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client

By Jun Xia and others
Although Federated Learning (FL) is promising in knowledge sharing for heterogeneous Artificial Intelligence of Thing (AIoT) devices, their training performance and energy efficacy are severely restricted in practical battery-driven scenarios due to the ``wooden barrel effect'' caused by the mismatch between homogeneous model paradigms and heterogeneous device capability. As a... Show more
July 9, 2024
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Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client
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