Resource-Constrained Federated Learning with Heterogeneous Labels and Models for Human Activity Recognition

Abstract

One of the most significant applications in pervasive computing for modeling user behavior is Human Activity Recognition (HAR). Such applications necessitate us to characterize insights from multiple resource-constrained user devices using machine learning techniques for effective personalized activity monitoring. On-device Federated Learning proves to be an extremely viable option for distributed and collaborative machine learning in such scenarios, and is an active area of research. However, there are a variety of challenges in addressing statistical (non-IID data) and model heterogeneities across users. In addition, in this paper, we explore a new challenge of interest – to handle heterogeneities in labels (activities) across users during federated learning. To this end, we propose a framework with two different versions for federated label-based aggregation, which leverage overlapping information gain across activities – one using Model Distillation Update, and the other using Weighted $\alpha$-update. Empirical evaluation on the Heterogeneity Human Activity Recognition (HHAR) dataset (with four activities for effective elucidation of results) indicates an average deterministic accuracy increase of at least $\sim$11.01% with the model distillation update strategy and $\sim$9.16% with the weighted $\alpha$-update strategy. We demonstrate the on-device capabilities of our proposed framework by using Raspberry Pi 2, a single-board computing platform.