The paper “Federated Multi-Task Attention for Cross-Individual Human Activity Recognition”, which sees the collaboration of WeNet’s coordinating partner University of Trento and Chinese partner Jilin University, has been accepted for the 31st International Joint Conference on Artificial Intelligence (IJCAI-2022), which will take place in Vienna, Austria in July.
For more insights on the paper’s content, read the abstract here below:
Federated Learning (FL) is an emerging privacy-aware machine learning technique that applies successfully to the collaborative learning of global models for Human Activity Recognition (HAR). As of now, the applications of FL for HAR assume that the data associated with diverse individuals follow the same distribution. However, this assumption is impractical in real-world scenarios where the same activity is frequently performed differently by different individuals. To tackle this issue, we propose FedMAT, a Federated Multi-task Attention framework for HAR, which extracts and fuses shared as well as individual-specific multi-modal sensor data features. Specifically, we treat the HAR problem associated with each individual as a different task and train a federated multi-task model, composed of a shared feature representation network in a central server plus multiple individual-specific networks with attention modules stored in decentralized nodes. In this architecture, the attention module operates as a mask that allows to learn individual-specific features from the global model, whilst simultaneously allowing for features to be shared among different individuals. We conduct extensive experiments based on publicly available HAR datasets, which are collected in both controlled environments and real-world scenarios. Numeric results verify that our proposed FedMAT significantly outperforms baselines not only in generalizing to existing individuals but also in adapting to new individuals.
More information on the event can be found on its official website