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FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning. (arXiv:2211.16669v1 [cs.LG])
Dec. 1, 2022, 2:10 a.m. | Young Geun Kim, Carole-Jean Wu
cs.CR updates on arXiv.org arxiv.org
Federated learning (FL) has emerged as a solution to deal with the risk of
privacy leaks in machine learning training. This approach allows a variety of
mobile devices to collaboratively train a machine learning model without
sharing the raw on-device training data with the cloud. However, efficient edge
deployment of FL is challenging because of the system/data heterogeneity and
runtime variance. This paper optimizes the energy-efficiency of FL use cases
while guaranteeing model convergence, by accounting for the aforementioned
challenges. …
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