FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning

This paper investigates efficient distributed training of a Federated Learning (FL) model over a wireless network of wireless devices. The communication iterations of the distributed training algorithm may be substantially deteriorated or even blocked by the effects of the devices' background t...

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Vydáno v:	IEEE transactions on wireless communications Ročník 23; číslo 9; s. 11076 - 11093
Hlavní autoři:	Mahmoudi, Afsaneh, Ghadikolaei, Hossein S., Barros Da Silva, Jose Mairton, Fischione, Carlo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.09.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Collision avoidance Communication communication efficiency communication protocols Communications traffic Computation Computer Science with specialization in Computer Communication cost-efficient algorithm Costs Criteria Datavetenskap med inriktning mot datorkommunikation Federated learning Frequency division multiple access latency Machine learning Machine learning algorithms Network latency Optimization Orthogonal Frequency Division Multiplexing Performance degradation Protocols resource allocation Resource management Traffic congestion Training unfolding federated learning Wireless communications Wireless networks
ISSN:	1536-1276, 1558-2248, 1558-2248
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Shrnutí:	This paper investigates efficient distributed training of a Federated Learning (FL) model over a wireless network of wireless devices. The communication iterations of the distributed training algorithm may be substantially deteriorated or even blocked by the effects of the devices' background traffic, packet losses, congestion, or latency. We abstract the communication-computation impacts as an 'iteration cost' and propose a cost-aware causal FL algorithm (FedCau) to tackle this problem. We propose an iteration-termination method that trade-offs the training performance and networking costs. We apply our approach when workers use the slotted-ALOHA, carrier-sense multiple access with collision avoidance (CSMA/CA), and orthogonal frequency-division multiple access (OFDMA) protocols. We show that, given a total cost budget, the training performance degrades as either the background communication traffic or the dimension of the training problem increases. Our results demonstrate the importance of proactively designing optimal cost-efficient stopping criteria to avoid unnecessary communication-computation costs to achieve a marginal FL training improvement. We validate our method by training and testing FL over the MNIST and CIFAR-10 dataset. Finally, we apply our approach to existing communication efficient FL methods from the literature, achieving further efficiency. We conclude that cost-efficient stopping criteria are essential for the success of practical FL over wireless networks.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1536-1276 1558-2248 1558-2248
DOI:	10.1109/TWC.2024.3378351