Semi Supervised Federated Learning with pseudo-labeling

Abstract

In order to efficiently learn from small amount of labeled data, this study presents pseudo- labeling using semi-supervised learning in a federated setting (Pseudo-FedSSL), a novel approach to semi-supervised federated learning that makes use of autoencoder- derived latent vectors and pseudo-labeling. Using this method, latent vectors from la- beled data are aggregated to create unique vectors for every class. Subsequently, the unlabeled data is pseudo-labeled by calculating the distance between each distinct vec- tor obtained from the labeled data and its latent vector. The class with the smallest distance determines the pseudo- label assignment, enhancing the model’s capacity to efficiently label unannotated samples. Pseudo-FSSL makes use of training an autoencoder and its transfer learning capacity to capture complex data representations and relationships. In addition to adding to the expanding body of federated learning approaches, the suggested pseudo-FSSL method offers a dependable and scalable alternative for semi-supervised learning, along with increasing classification accuracy.