research-article

Evaluation of federated learning aggregation algorithms: application to human activity recognition

Authors:

François Portet,

Philippe Lalanda,

German VegaAuthors Info & Claims

UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

Pages 638 - 643

https://doi.org/10.1145/3410530.3414321

Published: 12 September 2020 Publication History

Abstract

Pervasive computing promotes the integration of connected electronic devices in our living spaces in order to assist us through appropriate services. Two major developments have gained significant momentum recently: a better use of fog resources and the use of AI techniques. Specifically, interest in machine learning approaches for engineering applications has increased rapidly. \ This paradigm seems to fit the pervasive environment well. However, federated learning has been applied so far to specific services and remains largely conceptual. It needs to be tested extensively on pervasive services partially located in the fog. In this paper, we present experiments performed in the domain of Human Activity Recognition on smartphones in order to evaluate existing algorithms.

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Cited By

Dahal AMoulik SMukherjee R(2025)FeL-MAR: Federated learning based multi resident activity recognition in IoT enabled smart homesFuture Generation Computer Systems10.1016/j.future.2024.107552163(107552)Online publication date: Feb-2025
https://doi.org/10.1016/j.future.2024.107552
Selialia KChandio YAnwar F(2024)Mitigating Group Bias in Federated Learning for Heterogeneous DevicesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658954(1043-1054)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658954
Das SMudi RRahman MRabie KLi X(2024)Federated Reinforcement Learning for Wireless Networks: Fundamentals, Challenges and Future Research TrendsIEEE Open Journal of Vehicular Technology10.1109/OJVT.2024.34668585(1400-1440)Online publication date: 2024
https://doi.org/10.1109/OJVT.2024.3466858
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cover image ACM Conferences

UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

September 2020

732 pages

ISBN:9781450380768

DOI:10.1145/3410530

General Chairs:
Monica Tentori
CICESE, Mexico
,
Nadir Weibel
UC San Diego
,
Kristof Van Laerhoven
University of Siegen, Germany
,
Program Chairs:
Gregory Abowd
Georgia Tech
,
Flora Salim
RMIT, Australia

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Published: 12 September 2020

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UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers

September 12 - 17, 2020

Virtual Event, Mexico

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Cited By

Dahal AMoulik SMukherjee R(2025)FeL-MAR: Federated learning based multi resident activity recognition in IoT enabled smart homesFuture Generation Computer Systems10.1016/j.future.2024.107552163(107552)Online publication date: Feb-2025
https://doi.org/10.1016/j.future.2024.107552
Selialia KChandio YAnwar F(2024)Mitigating Group Bias in Federated Learning for Heterogeneous DevicesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658954(1043-1054)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658954
Das SMudi RRahman MRabie KLi X(2024)Federated Reinforcement Learning for Wireless Networks: Fundamentals, Challenges and Future Research TrendsIEEE Open Journal of Vehicular Technology10.1109/OJVT.2024.34668585(1400-1440)Online publication date: 2024
https://doi.org/10.1109/OJVT.2024.3466858
Aouedi OSacco AKhan LNguyen DGuizani M(2024)Federated Learning for Human Activity Recognition: Overview, Advances, and ChallengesIEEE Open Journal of the Communications Society10.1109/OJCOMS.2024.34842285(7341-7367)Online publication date: 2024
https://doi.org/10.1109/OJCOMS.2024.3484228
Sheikholeslami SNg PLiu HYu YPlataniotis K(2024)Towards Collaborative Multimodal Federated Learning for Human Activity Recognition in Smart Workplace Environments2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)10.1109/ICASSPW62465.2024.10627018(635-639)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSPW62465.2024.10627018
Liu XDeng YNallanathan ABennis M(2024)Federated Learning and Meta Learning: Approaches, Applications, and DirectionsIEEE Communications Surveys & Tutorials10.1109/COMST.2023.333091026:1(571-618)Online publication date: Sep-2025
https://doi.org/10.1109/COMST.2023.3330910
Shaik TTao XLi LHiggins NGururajan RZhou XYong J(2024)Clustered FedStack: Intermediate Global Models with Bayesian Information CriterionPattern Recognition Letters10.1016/j.patrec.2023.12.004177(121-127)Online publication date: Jan-2024
https://doi.org/10.1016/j.patrec.2023.12.004
Konak Ovan de Water RDöring VFiedler TLiebe LMasopust LPostnov KSauerwald FTreykorn FWischmann AGjoreski HLuštrek MArnrich B(2023)HARE: Unifying the Human Activity Recognition Engineering WorkflowSensors10.3390/s2323957123:23(9571)Online publication date: 2-Dec-2023
https://doi.org/10.3390/s23239571
Stefanidis VVerginadis YMentzas G(2023)MulticloudFL: Adaptive Federated Learning for Improving Forecasting Accuracy in Multi-Cloud EnvironmentsInformation10.3390/info1412066214:12(662)Online publication date: 14-Dec-2023
https://doi.org/10.3390/info14120662
Wang TDu YGong YChoo KGuo Y(2023)Applications of Federated Learning in Mobile Health: Scoping ReviewJournal of Medical Internet Research10.2196/4300625(e43006)Online publication date: 1-May-2023
https://doi.org/10.2196/43006
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