  Wearable data serves various health monitoring purposes, such as determining
activity states based on user behavior and providing tailored exercise
recommendations. However, the individual data perception and computational
capabilities of wearable devices are limited, often necessitating the joint
training of models across multiple devices. Federated Human Activity
Recognition (HAR) presents a viable research avenue, allowing for global model
training without the need to upload users' local activity data. Nonetheless,
recent studies have revealed significant privacy concerns persisting within
federated learning frameworks. To address this gap, we focus on investigating
privacy leakage issues within federated user behavior recognition modeling
across multiple wearable devices. Our proposed system entails a federated
learning architecture comprising $N$ wearable device users and a parameter
server, which may exhibit curiosity in extracting sensitive user information
from model parameters. Consequently, we consider a membership inference attack
based on a malicious server, leveraging differences in model generalization
across client data. Experimentation conducted on five publicly available HAR
datasets demonstrates an accuracy rate of 92\% for malicious server-based
membership inference. Our study provides preliminary evidence of substantial
privacy risks associated with federated training across multiple wearable
devices, offering a novel research perspective within this domain.
