Sensor-based Human Activity Recognition (HAR) is increasingly utilized to automatically detect daily human activities, stimulated by the widespread adoption of wearable devices. To protect user privacy, the Federated Learning (FL) framework is often applied in sensor-based HAR, ensuring that raw data remains within the confines of the wearable device. This data isolation typically results in unlabeled raw data, as labeling is costly, time-consuming, and would require sending data to external experts. Moreover, implementing sensor-based HAR in real-world scenarios faces challenges such as computational constraints on wearable devices and the non-IID nature of FL data. In response, we propose a novel framework, LiCAFeL-STC, designed to train sensor-based HAR under conditions where only unlabeled data is available on wearable devices. These devices are limited in computational resources, and the data exhibits high heterogeneity, simulating the non-IID nature of FL data. Our framework employs signal transformation classification as an auxiliary self-supervised learning (SSL) technique to leverage large amounts of unlabeled data on wearable devices and incorporates a clustering mechanism to group similar devices, mitigating the non-IID problem. Our findings demonstrate that LiCAFeL-STC can outperform both the conventional method and baseline frameworks under similar experimental settings.

Detecting an anomaly is an essential task in the manufacturing operation. Due to the vast adaptation of machines for industry, AI has become an indispensable part of detecting anomalous instances. However, data scarcity and cost allocation pose a significant challenge for individual companies to train a model alone. Therefore, in Industries 5.0, companies need to collaborate to achieve the common goal. On the other hand, they also need to disclose sensitive information according to General Data Protection Regulation (GDPR). This work presents a secure collaborative framework with Federated Learning to enable the development of an anomaly detection model among multiple machines or clients in different companies. The proposed framework performs tasks such as managing secure connections among clients, transforming client data to a processable format, and conducting model training between clients simultaneously.