Towards a Resource-Efficient Semi-Asynchronous Federated Learning for Heterogeneous Devices

Sasindran, Z and Yelchuri, H and Prabhakar, TV (2024) Towards a Resource-Efficient Semi-Asynchronous Federated Learning for Heterogeneous Devices. In: 30th National Conference on Communications, NCC 2024, 28 February 2024 through 2 March 2024, Chennai.

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Abstract

Our proposed resource-efficient semi-asynchronous federated learning (RE-SAFL) approach presents a comprehensive and effective solution for training large models such as Automatic Speech Recognition (ASR) models in a distributed and semi-asynchronous manner. In our research, we highlight the importance of employing a resource-efficient work allocation approach when deploying complex tasks such as ASR in real-time on edge devices such as mobile phones. To validate our approach, we conducted experiments on a real FL test-bed using Android-based mobile devices. By addressing the resource constraints of client devices and optimizing work allocation, our RE-SAFL framework opens up new possibilities for training large models in semi-asynchronous federated environments. © 2024 IEEE.

Item Type:	Conference Paper
Publication:	2024 National Conference on Communications, NCC 2024
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Allocation approach; Automatic speech recognition; Complex task; Effective solution; Heterogeneous devices; Large models; Learning approach; Recognition models; Resource-efficient; Semi-asynchronoi federated learning, Speech recognition
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited:	27 Aug 2024 13:28
Last Modified:	27 Aug 2024 13:28
URI:	http://eprints.iisc.ac.in/id/eprint/84855

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