Federated mobile sensing for activity recognition

Stefanos Laskaridis, Dimitris Spathis, Mário Almeida

Abstract: Despite advances in hardware and software enabling faster on-device inference, training Deep Neural Networks (DNN) models has largely been a long-running task over TBs of collected user data in centralised repositories. Federated Learning has emerged as an alternative, privacy-preserving paradigm to train models without accessing directly on-device data, by leveraging device resources to create per client updates and aggregate centrally. This has been applied to various tasks, ranging from next-word prediction to automatic speech recognition (ASR). In this tutorial, we recognise on-device sensing as a privacy-sensitive task and build a federated learning system from scratch to showcase how to train a model for accelerometer-based activity recognition in a federated manner. In addition, we present the current landscape and challenges in the realm of federated learning and mobile sensing and provide guidelines on how to build such systems in a privacy-preserving and scalable manner.