In recent years, the widespread diffusion of smart pervasive devices able to provide AI-based services has encouraged research in the definition of new distributed learning paradigms. Federated Learning (FL) is one of the most recent approaches which allows devices to collaborate to train AI-based models, whereas guarantying privacy and lower communication costs. Although different studies on FL have been conducted, a general and modular architecture capable of performing well in different scenarios is still missing. Following this direction, this paper proposes a general FL framework whose validity is assessed by considering a distributed activity recognition scenario in which users' personal devices are employed as the basis of the sensing infrastructure. Experimental analysis was performed to evaluate the effectiveness of the architecture as compared with a centralized approach, under different settings. Results demonstrate the versatility and functionality of the proposed solution.

Concone F., Ferdico C., Lo Re G., Morana M. (2022). A Federated Learning Approach for Distributed Human Activity Recognition. In In Proceedings of the 2022 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 269-274). Institute of Electrical and Electronics Engineers Inc. [10.1109/SMARTCOMP55677.2022.00066].

A Federated Learning Approach for Distributed Human Activity Recognition

Concone F.
;
Ferdico C.;Lo Re G.;Morana M.
2022-07-14

Abstract

In recent years, the widespread diffusion of smart pervasive devices able to provide AI-based services has encouraged research in the definition of new distributed learning paradigms. Federated Learning (FL) is one of the most recent approaches which allows devices to collaborate to train AI-based models, whereas guarantying privacy and lower communication costs. Although different studies on FL have been conducted, a general and modular architecture capable of performing well in different scenarios is still missing. Following this direction, this paper proposes a general FL framework whose validity is assessed by considering a distributed activity recognition scenario in which users' personal devices are employed as the basis of the sensing infrastructure. Experimental analysis was performed to evaluate the effectiveness of the architecture as compared with a centralized approach, under different settings. Results demonstrate the versatility and functionality of the proposed solution.
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
978-1-6654-8152-6
Concone F., Ferdico C., Lo Re G., Morana M. (2022). A Federated Learning Approach for Distributed Human Activity Recognition. In In Proceedings of the 2022 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 269-274). Institute of Electrical and Electronics Engineers Inc. [10.1109/SMARTCOMP55677.2022.00066].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/567563
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