Multiple Parallel Federated Learning via Over-the-Air Computation

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 pr...

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Bibliographic Details
Published in:IEEE open journal of the Communications Society Vol. 3; pp. 1252 - 1264
Main Authors: Shi, Gaoxin, Guo, Shuaishuai, Ye, Jia, Saeed, Nasir, Dang, Shuping
Format: Journal Article
Language:English
Published: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Cellular communication
Collaborative work
Computation
Computational modeling
Convergence
Decoupling
Device selection
Federated learning
Greedy algorithms
Impact analysis
multiple access channel
Optimization
over-the-air computation
Performance evaluation
Radio equipment
receiver combiner
Receivers
Servers
Superposition (mathematics)
Task analysis
Task scheduling
ISSN:2644-125X, 2644-125X
Online Access:Get full text
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Summary: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.
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ISSN:2644-125X
2644-125X
DOI:10.1109/OJCOMS.2022.3194821