Device or User: Rethinking Federated Learning in Personal-Scale Multi-Device Environments

We are witnessing a trend of users owning multiple data-generating wearable and IoT devices that continuously capture sensor data pertaining to a user's activities and context. Federated Learning is a potential technique to derive meaningful insights from this sensor data in a privacy-preserving way without revealing the raw sensor data to a central server. In this paper, we introduce a new problem setting in this multi-device context called Federated Learning in Multi-Device Local Networks (FL-MDLN). We identify core challenges for FL-MDLN in relation to its federation architecture, and statistical and systems heterogeneity across multiple users and multiple devices. Then, we introduce a new user-as-client (UAC) federation architecture, and propose various device selection strategies to counter statistical and systems heterogeneity in FL-MDLN. Early empirical findings show that our proposed techniques improve model test accuracy as well as battery power efficiency in FL. Based on these findings, we elucidate open research questions and future work in FL-MDLN.