May 15, 2024, 4:42 a.m. | Jun Xia, Yiyu Shi

cs.LG updates on

arXiv:2405.08183v1 Announce Type: new
Abstract: 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 result, due to various kinds of differences among devices, it is hard for existing FL methods to conduct training effectively in energy-constrained scenarios, such as …

abstract aiot artificial artificial intelligence arxiv battery client cs.lg devices energy federated learning intelligence knowledge performance practical training type via

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