Multiple Parallel Federated Learning via Over-the-Air Computation

Gaoxin Shi, Shuaishuai Guo, Jia Ye, Nasir Saeed, Shuping Dang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This paper investigates multiple parallel federated learning in cellular networks, where a base station schedules several FL tasks in parallel and each task has a group of devices involved. To reduce the communication overhead, over-the-air computation is introduced by utilizing the superposition property of multiple access channels (MAC) to accomplish the aggregation step. Since all devices use the same radio resource to transfer their local updates to the BS, in order to separate the received signals of different tasks, we use the zero-forcing receiver combiner to mitigate the mutual interference across different groups. Besides, we analyze the impact of receiver combiner and device selection on the convergence of our multiple parallel FL framework. Also, we formulate an optimization problem that jointly considers receiver combiner vector design and device selection for improving FL performance. We address the problem by decoupling it into two sub-problems and solve them alternatively, adopting successive convex approximation (SCA) to derive the receiver combiner vector, and then solve the device scheduling problem with a greedy algorithm. Simulation results demonstrate that the proposed framework can effectively solve the straggler issue in FL and achieve a near-optimal performance on all tasks.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Open Journal of the Communications Society
StatePublished - Jul 28 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledgements: The work is supported in part by the National Natural Science Foundation of China under Grant 62171262, in part by Shandong Provincial Natural Science Foundation under Grant ZR2021YQ47, in part by the and in part by Major Scientific and Technological Innovation Project of Shandong Province under Grant 2020CXGC010109 and in part by Tashan Young Scholar under Grant No. tsqn201909043.


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