Towards a Resource-Efficient Semi-Asynchronous Federated Learning for Heterogeneous Devices | IEEE Conference Publication | IEEE Xplore

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


Abstract:

Our proposed resource-efficient semi-asynchronous federated learning (RE-SAFL) approach presents a comprehensive and effective solution for training large models such as ...Show More

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.
Date of Conference: 28 February 2024 - 02 March 2024
Date Added to IEEE Xplore: 05 April 2024
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Conference Location: Chennai, India

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