GraFeHTy: Graph Neural Network using Federated Learning for Human Activity Recognition

Human Activity Recognition (HAR) from sensor measurements is still challenging due to noisy or lack of la-belled examples and issues concerning data privacy. Training a traditional centralized machine learning (ML) model for HAR is constrained by infrastructure availability, network connectivity, latency issues etc. Issues regarding labels of user measurements can be tackled by using semi-supervised learning while issues regarding privacy concerns can be addressed by increasingly popular Federated Learning (FL). In this work, we propose a novel algorithm GraFeHTy, a Graph Convolution Network (GCN) trained in a federated setting to alleviate these key obstructions for HAR. We construct a similarity graph from sensor measurements for each user and apply a GCN to perform semi-supervised classification of human activities by leveraging inter-relatedness and closeness of activities. The weights of the GCN are trained using federated learning where each user performs gradient descent using their local data and share only the updated weights with a central server for aggregation. The GCN helps in accurate detection of unlabelled or noisy labels in the activity sequence by borrowing information from similar labelled nodes. The federated setting for training these models ensures that user privacy is respected by transferring only the learned representations out of the device to a central server. By avoiding transfer of raw data, the algorithm also ensures that training a HAR model is not constrained by infrastructure availability in central server or low network bandwidth from edge devices. Our proposed algorithm performs better than the baseline feed-forward federated learning model in terms of both accuracy and computational complexity.