On Federated Learning with Energy Harvesting Clients
On Federated Learning with Energy Harvesting Clients
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DOI：
10.1109/icassp43922.2022.9746608
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Cong Shen;Jing Yang;Jie Xu
中科院分区：
文献类型：
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作者：
Cong Shen;Jing Yang;Jie Xu
迎合物品设备的扩散和在边缘的分发机器学习，我们提出了一个能量收获联合学习（EHFL）框架（EHFL）框架，在本文中引入EH，这意味着客户可以保证参与任何fl的能力，这使我们能够随机捕获易于范围的设备。参与的客户，对于具有非注册损失函数的并行和局部随机梯度下降（SGD）。
Catering to the proliferation of Internet of Things devices and distributed machine learning at the edge, we propose an energy harvesting federated learning (EHFL) framework in this paper. The introduction of EH implies that a client’s availability to participate in any FL round cannot be guaranteed, which complicates the theoretical analysis. We derive novel convergence bounds that capture the impact of time-varying device availabilities due to the random EH characteristics of the participating clients, for both parallel and local stochastic gradient descent (SGD) with non-convex loss functions. The results suggest that having a uniform client scheduling that maximizes the minimum number of clients throughout the FL process is desirable, which is further corroborated by the numerical experiments using a real-world FL task and a state-of-the-art EH scheduler.