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 MoreMetadata
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.
Published in: 2024 National Conference on Communications (NCC)
Date of Conference: 28 February 2024 - 02 March 2024
Date Added to IEEE Xplore: 05 April 2024
ISBN Information: