2023
DOI: 10.1186/s13677-023-00515-6
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Adaptive device sampling and deadline determination for cloud-based heterogeneous federated learning

Deyu Zhang,
Wang Sun,
Zi-Ang Zheng
et al.

Abstract: As a new approach to machine learning, Federated learning enables distributned traiing on edge devices and aggregates local models into a global model. The edge devices that participate in federated learning are highly heterogeneous in terms of computing power, device state, and data distribution, making it challenging to converge models efficiently. In this paper, we propose FedState, which is an adaptive device sampling and deadline determination technique for cloud-based heterogeneous federated learning. Sp… Show more

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