  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. Our method employs a third-party dataset unrelated to model
training. Using KL divergence as a loss function for fine-tuning, we aim to
align the predicted probability distribution on forgotten data with the
third-party dataset. Additionally, we introduce a membership inference
evaluation method to assess unlearning effectiveness. Experimental results
across diverse datasets show our method achieves unlearning accuracy comparable
to \textit{retraining} methods, resulting in speedups ranging from hundreds to
thousands.
