FedNaWi: Selecting the Befitting Clients for Robust Federated Learning in IoT Applications

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Abstract

Federated Learning (FL) is an important privacy-preserving learning paradigm that is expected to play an essential role in the future Intelligent Internet of Things (IoT). However, model training in FL is vulnerable to noise and the statistical heterogeneity of local data across IoT clients. In this paper, we propose FedNaWi, a “Go Narrow, Then Wide” client selection method that speeds up the FL training, achieves higher model performance, while requiring no additional data or sensitive information transfer from clients. Our method first selects reliable clients (i.e., going narrow) which allows the global model to quickly improve its performance and then includes less reliable clients (i.e., going wide) to exploit more IoT data of clients to further improve the global model. To profile client utility, we introduce a unified Bayesian framework to model the client utility at the FL server, assisted by a small amount of auxiliary data. We conduct extensive evaluations with 5 state-of-the-art FL methods, on 3 IoT tasks and under 7 different types of label and feature noise. We build an FL testbed with 38 IoT nodes (20 nodes run on Raspberry Pi 4B and 18 nodes run on Jetson Nano) for the evaluation. Our results show that FedNaWi improves the FL accuracy substantially and significantly reduces energy consumption. In particular, FedNaWi improves the accuracy from 35% to 75% in the non-IID Dirichlet setting, and reduces the average energy consumption by 55%.
Original languageEnglish
Title of host publicationProceedings of the 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
PublisherIEEE
Pages402-410
Number of pages9
ISBN (Electronic)979-8-3503-0052-9
ISBN (Print)979-8-3503-0053-6
DOIs
Publication statusPublished - 2023
Event2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) - Madrid, Spain
Duration: 11 Sept 202314 Sept 2023

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
PublisherIEEE
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
Country/TerritorySpain
CityMadrid
Period11/09/2314/09/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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