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Latency-Aware Node Selection in Federated Learning

Abstract

Federated learning (FL) relies on the frequent exchange of model parameters between clients and the aggregator to achieve efficient model convergence. However, network latency presents a significant challenge, particularly in congested edge/IoT scenarios, hindering the efficiency and effectiveness of distributed machine learning (ML). While existing solutions often depend on hard-coded topologies, addressing this challenge is critical to unlocking FL's full potential in real-world scenarios. This paper proposes a novel approach to mitigate network latency issues by introducing a threefold functionality: latency-aware client selection, latency-aware aggregator assignment, and consistent replication of training progress. Our proof of concept provides a scalable and robust solution to alleviate latency's impact and improve the efficiency of distributed ML operations. Through this research, we aim to advance the field of FL by offering practical solutions that enhance performance and resilience in latency-sensitive environments.
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Category

Academic chapter

Language

English

Affiliation

  • SINTEF Digital / Sustainable Communication Technologies

Year

2024

Publisher

IEEE (Institute of Electrical and Electronics Engineers)

Book

2024 IFIP Networking Conference (IFIP Networking)

ISBN

9783903176638

Page(s)

598 - 600

View this publication at Norwegian Research Information Repository