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FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized.
Privacy in Multimodal Federated Human Activity Recognition
This work studies the impact of privacy in federated HAR at a user, environment, and sensor level.
Can Fair Federated Learning reduce the need for Personalisation?
For situations where the federated model provides a lower accuracy than a model trained entirely locally by a client, personalisation improves the accuracy of the pre-trained federated weights to be similar to or exceed those of the local client model.
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