
A lightweight machine unlearning method is proposed to efficiently remove a subset of a client's training data from the federated learning model for human activity recognition, without compromising the model's performance on the remaining data.


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efficient-federated-unlearning-for-protecting-user-privacy-in-human-activity-recognition


Efficient Federated Unlearning for Protecting User Privacy in Human Activity Recognition


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MESEN exploits unlabeled multimodal data to extract effective unimodal features, thereby enhancing the performance of unimodal human activity recognition with few labeled samples.


mesen-leveraging-unlabeled-multimodal-data-to-enhance-unimodal-human-activity-recognition-with-few-labels


MESEN: Leveraging Unlabeled Multimodal Data to Enhance Unimodal Human Activity Recognition with Few Labels
