AttFL: A Personalized Federated Learning Framework for Time-series Mobile and Embedded Sensor Data Processing

This work presents AttFL, a federated learning framework designed to continuously improve a personalized deep neural network for efficiently analyzing time-series data generated from mobile and embedded sensing applications. To better characterize time-series data features and efficiently abstract model parameters, AttFL appends a set of attention modules to the baseline deep learning model and exchanges their feature map information to gather collective knowledge across distributed local devices at the server. The server groups devices with similar contextual goals using cosine similarity, and redistributes updated model parameters for improved inference performance at each local device. Specifically, unlike previously proposed federated learning frameworks, AttFL is designed specifically to perform well for various recurrent neural network (RNN) baseline models, making it suitable for many mobile and embedded sensing applications producing time-series sensing data. We evaluate the performance of AttFL and compare with five state-of-the-art federated learning frameworks using three popular mobile/embedded sensing applications (e.g., physiological signal analysis, human activity recognition, and audio processing). Our results obtained from CPU core-based emulations and a 12-node embedded platform testbed shows that AttFL outperforms all alternative approaches in terms of model accuracy and communication/computational overhead, and is flexible enough to be applied in various application scenarios exploiting different baseline deep learning model architectures.