Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase
This work addresses privacy and memory issues for AI applications on micro-controllers, such as keyword spotting, but is incremental as it builds on existing split federated learning concepts.
The paper tackled the problem of implementing large models on edge devices with memory constraints by proposing a split federated learning framework, achieving over 90% accuracy on a Chinese digits dataset and a 13.89% higher accuracy compared to a state-of-the-art FL implementation on an English digits dataset.
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.