Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase

Jingtao Li; Runcong Kuang

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arXiv · 2210.01961

Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase

Jingtao Li·Runcong Kuang

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Abstract

Nowadays, AI companies improve service quality by aggressively collecting users' data generated by edge devices, which jeopardizes data privacy. To prevent this, Federated Learning is proposed as a private learning scheme, using which users can locally train the model without collecting users' raw data to servers. However, for machine-learning applications on edge devices that have hard memory constraints, implementing a large model using FL is infeasible. To meet the memory requirement, a recent collaborative learning scheme named split federal learning is a potential solution since it keeps a small model on the device and keeps the rest of the model on the server. In this work, we implement a simply SFL framework on the Arduino board and verify its correctness on the Chinese digits audio dataset for keyword spotting application with over 90% accuracy. Furthermore, on the English digits audio dataset, our SFL implementation achieves 13.89% higher accuracy compared to a state-of-the-art FL implementation.

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BibTeX RIS

Jingtao Li, Runcong Kuang. 2022-10-04. Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase. https://arxiv.org/abs/2210.01961

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