Sasindran, Z and Yelchuri, H and Prabhakar, TV (2023) Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices. In: 2023 International Joint Conference on Neural Networks, IJCNN 2023, 18-23 June 2023, Gold Coast, Australia.
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Abstract
Federated learning (FL) has evolved as a prominent method for edge devices to cooperatively create a unified prediction model while securing their sensitive training data local to the device. Despite the existence of numerous research frameworks for simulating FL algorithms, they do not facilitate comprehensive deployment for automatic speech recognition tasks on heterogeneous edge devices. This is where Ed-Fed, a comprehensive and generic FL framework, comes in as a foundation for future practical FL system research. We also propose a novel resource-aware client selection algorithm to optimise the waiting time in the FL settings. We show that our approach can handle the straggler devices and dynamically set the training time for the selected devices in a round. Our evaluation has shown that the proposed approach significantly optimises waiting time in FL compared to conventional random client selection methods. © 2023 IEEE.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Proceedings of the International Joint Conference on Neural Networks |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Automatic speech recognition; Federated learning system; Learning frameworks; Prediction modelling; Research frameworks; Resource aware; Selection algorithm; Systems research; Training data; Waiting time, Speech recognition |
| Department/Centre: | Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology) |
| Date Deposited: | 04 Nov 2023 04:16 |
| Last Modified: | 04 Nov 2023 04:16 |
| URI: | https://eprints.iisc.ac.in/id/eprint/83163 |
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