Towards Understanding the Impact of Participant and its Wearable Devices in Federated Learning

The popularity of wearable smart devices has increased due to their seamless monitoring of vital signs during daily activities. Federated learning leverages these devices along with participants’ smartphones to fine-tune pre-trained models. Moreover, calibrating the differences between wearables and smartphones in terms of sampling rates, orientations, activity correlation, battery power, and other factors is challenging. Thus, the paper introduces a participant and wearable selection cross-device federated learning approach. It leverages criteria such as the activity wearable(s) relationship, data quality, battery life, sampling rate, and so on to perform the wearable selection. The server evaluates and estimates the utility of each participant and selects those with higher utility in each communication round. We then figure out the optimal weighted contribution of each participant to perform robust aggregation. We also use knowledge distillation techniques to develop a high-performing and lightweight wearable model. Finally, we conduct simulation and real-world experiments on existing datasets and compare our approach with state-of-the-art. The result shows an improvement of $3\!\!-\!\!4\%$ in accuracy via fine-tuning from selected wearable data.