Joint Bandwidth Allocation, Computation Control, and Device Scheduling for Federated Learning with Energy Harvesting Devices

Li Zeng1; Dingzhu Wen1; Guangxu Zhu2; Changsheng You3; Qimei Chen4; Yuanming Shit1

10.1109/IEEECONF56349.2022.10051949

2022-10-31

56th Asilomar Conference on Signals, Systems, and Computers

1058-6393

978-1-6654-5907-5

2022 56th Asilomar Conference on Signals, Systems, and Computers

1164-1168

31 Oct.-2 Nov. 2022

Pacific Grove, CA, USA

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Federated learning (FL) is a promising technique for distilling artificial intelligence from massive data distributed in Internet-of-Things (IoT) networks while keeping their privacy. However, the efficient deployment of FL faces several challenges due to e.g., limited radio resources, computation capability, and battery capacity of IoT devices. To address these challenges, in this work, the energy harvesting technique is first enabled on IoT devices for supporting their long-term training. Then, the convergence rate of the FL algorithm is derived, which indicates that for reducing the learning latency, the data utility, i.e., the number of training samples, should be maximized in each training iteration. To this end, a data utility maximization problem for each iteration is formulated, under the constraints of limited time, bandwidth, computation frequency, and energy supply. The problem is mixed-integer and non-convex, and hence NP-hard. A joint bandwidth allocation, computation control, and device selection scheme is proposed. In the scheme, an energy-efficient training data contribution indicator is first derived for each device, and then a sequential device scheduling scheme is designed.

Federated learning energy harvesting device selection resource allocation

Others

English

CPCI-S

Computer Science ; Engineering ; Telecommunications

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic ; Telecommunications

WOS:000976687600214

IEEE

1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China
2.Shenzhen Research Institute of Big Data, Shenzhen, China
3.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
4.School of Electronic Information, Wuhan University, Wuhan, China

Li Zeng,Dingzhu Wen,Guangxu Zhu,et al. Joint Bandwidth Allocation, Computation Control, and Device Scheduling for Federated Learning with Energy Harvesting Devices[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1164-1168.