Federated Unlearning for Human Activity Recognition

Understand

The rapid evolution of Internet of Things (IoT) technology has spurred the widespread adoption of Human Activity Recognition (HAR) in various daily life domains.

• Federated Learning (FL) is frequently utilized to build a global HAR model by aggregating user contributions without transmitting raw individual data.
• Despite substantial progress in user privacy protection with FL, challenges persist.
• Regulations like the General Data Protection Regulation (GDPR) empower users to request data removal, raising a new query in FL: How can a HAR client request data removal without compromising other clients' privacy? In response, we propose a lightweight machine unlearning method for refining the FL HAR model by selectively removing a portion of a client's training data.