Energy Harvesting Aware Client Selection for Over-the-Air Federated Learning
Energy Harvesting Aware Client Selection for Over-the-Air Federated Learning
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DOI：
10.1109/globecom48099.2022.10001136
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Caijuan Chen;Yi-Han Chiang;Hai Lin;John C.S. Lui;Yusheng Ji
中科院分区：
文献类型：
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作者：
Caijuan Chen;Yi-Han Chiang;Hai Lin;John C.S. Lui;Yusheng Ji
联邦学习（FL）被广泛认为是一种很有前途的分布式机器学习技术，它利用设备上的计算，同时保护客户端的数据隐私。为了使FL适应无线网络，空中（OTA）计算，它利用无线波形的叠加性质，可以防止过度消耗的通信资源。而能量收集技术可以克服客户端的能量限制，实现持久计算。尽管现有的工作致力于OTA FL从各个方面，他们大多忽略了联合执行客户端选择和能量收集设备的能量管理。在本文中，我们研究了OTA FL的客户端选择和能量管理的组合问题，并将其表示为一个非线性整数规划（NIP）问题，以最小化最优间隙。为了解决NIP问题，我们提出了一个客户端选择方案，联合考虑信道状态信息，剩余电池容量和数据集大小。我们的仿真结果表明，所提出的解决方案优于其他比较方案在各种参数设置。
Federated learning (FL) has been widely regarded as a promising distributed machine learning technology that utilizes on-device computation while protecting clients' data privacy. To adapt FL to wireless networks, the over-the-air (OTA) computation, which employs the superposition nature of wireless waveforms, can prevent excessive consumption of the communication resources. However, energy harvesting technology can overcome the energy limitation of clients to realize durable computation. Despite the existing works devoted to OTA FL from various aspects, they mostly neglect jointly performing client selection and energy management for energy harvesting devices. In this paper, we investigate the combined problem of client selection and energy management for OTA FL and formulate it as a nonlinear integer programming (NIP) problem to minimize the optimality gap. To solve the NIP problem, we propose a client selection scheme that jointly considers channel state information, residual battery capacities, and dataset size. Our simulation results show that the proposed solution outperforms other comparison schemes within various parameter settings.