Private, Fair and Secure Collaborative Learning Framework for Human Activity Recognition

Federated learning (FL), a decentralized machine learning technique, enhances privacy by enabling multiple devices to collaboratively train a model without transferring data to a central server. FL is used in Human Activity Recognition (HAR) problems, where multiple users generating private wearable data share models with a server to learn a useful global model. However, FL may compromise data privacy through model information sharing during training. Moreover, it adheres to a one-size-fits-all approach toward data privacy, potentially neglecting varied user preferences in collaborative scenarios such as HAR. In response to these challenges, this paper presents a collaborative learning framework integrating differential privacy (DP) and FL, thus providing a tailored approach to privacy protection. While some existing works integrate DP and FL, they do not allow clients to have different privacy preferences. In this work, we introduce a framework that allows different clients to have different privacy preferences and hence more flexibility in terms of privacy. In our framework, DP adds individualized noise to individual clients' gradient updates for privacy. However, such noised updates can also be interpreted as an attack on the FL system. Defending against these attacks might result in excluding honest private clients altogether from training, posing a fairness concern. On the other hand, not having any defensive measures might allow malicious users to attack the system, posing a security issue. Thus, to address security and fairness, our framework incorporates a client selection strategy that protects the global model from malicious clients and provides fair model access to honest private clients. We have demonstrated the effectiveness of our system on a HAR dataset and provided insights into our framework's privacy, utility, and fairness.