FedSTEM-ADL: A Federated Spatial-Temporal Episodic Memory Model for ADL Prediction

Learning of Activities of Daily Living (ADLs) provides insights into an individual’s habits, lifestyle, and well-being. However, it is crucial to address data privacy concerns in practical situations when learning the ADL routines of individuals. In this paper, we introduce FedSTEM-ADL, a federated spatial-temporal episodic memory model to address this privacy issue. FedSTEM-ADL utilizes a federation of Spatial-Temporal Episodic Memory for ADLs (STEM-ADL) for federated learning, wherein multiple local STEM-ADL models from individual users are combined into a global model while preserving the privacy of the original data. Specifically, each local model is designed to learn the spatio-temporal ADL routines of an individual user, representing them as ADL events and sequences of such events as episode patterns. The global model then integrates the local models without referring to the underlying individual data, thus addressing privacy concerns in multi-user ADL analysis. We conduct a series of experiments based on both pseudo and real-world multi-user ADL datasets. The results show that FedSTEM-ADL is able to learn global ADL models in an efficient manner and consistently outperforms the baseline models in the task of next ADL event prediction.