Federated Learning for Human Activity Recognition: Overview, Advances, and Challenges

[en] Human Activity Recognition (HAR) has seen remarkable advances in recent years, drivenby the widespread use of wearable devices and the increasing demand for personalized healthcare andactivity tracking. Federated Learning (FL) is a promising paradigm for HAR that enables the collaborativetraining of machine learning models on decentralized devices while preserving data privacy. It improvesnot only data privacy but also training efficiency as it utilizes the computing power and data of potentiallymillions of smart devices for parallel training. In addition, it helps end-user devices avoid sending users’private data to the cloud, eliminates the need for a network connection, and saves the latency of back-and-forth communication. FL also offers significant advantages for communication by reducing the amount ofdata transmitted over the network, alleviating network congestion and reducing communication costs. Bydistributing the training process across devices, FL minimizes the need for centralized data storage andprocessing, leading to more scalable and resilient systems. This paper provides a comprehensive surveyof the integration of FL into HAR applications. Unlike existing reviews, this paper uniquely focuses onthe intersection of FL and HAR, providing an in-depth analysis of recent advances and their practicalimplications. We explore key advances in FL-based HAR methodologies, including model architectures,optimization techniques, and different applications. Furthermore, we highlight the major challenges andfuture research questions in this domain, such as model personalization and robustness, privacy concerns,concept drift, and the limited capacity of edge devices.

Disciplines :

Sciences informatiques

Auteur, co-auteur :

AOUEDI, Ons ; University of Luxembourg

Sacco, Alessio ; DAUIN, Politecnico di Torino, Turin, Italy

Khan, Latif U. ; Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence, United Arab Emirates

Nguyen, Dinh C. ; Department of Electrical and Computer Engineering, The University of Alabama, Huntsville, USA

Guizani, Mohsen ; Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence, United Arab Emirates

Co-auteurs externes :

yes

Langue du document :

Anglais

Titre :

Federated Learning for Human Activity Recognition: Overview, Advances, and Challenges

Date de publication/diffusion :

octobre 2024

Titre du périodique :

IEEE Open Journal of the Communications Society

eISSN :

2644-125X

Maison d'édition :

Institute of Electrical and Electronics Engineers (IEEE)

Pagination :

1-1

Peer reviewed :

Peer reviewed vérifié par ORBi

Focus Area :

Computational Sciences

URL complémentaire :

http://xplorestaging.ieee.org/ielx8/8782661/8901158/10726594.pdf?arnumber=10726594

Organisme subsidiant :

HORIZON EUROPE Digital, Industry and Space

Disponible sur ORBilu :

depuis le 25 octobre 2024

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225 (dont 4 Unilu)

Nombre de téléchargements

353 (dont 1 Unilu)

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