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To satisfy the broad applications and insatiable hunger for deploying lowlatency multimedia data classification and data privacy in a cloud-basedsetting, federated learning (FL) has emerged as an important learning paradigm.For the practical cases involving limited computational power and onlyunlabeled data in many wireless communications applications, this workinvestigates FL paradigm in a resource-constrained and label-missingenvironment. Specifically, we propose a novel framework of UFed-GAN:Unsupervised Federated Generative Adversarial Network, which can captureuser-side data distribution without local classification training. We alsoanalyze the convergence and privacy of the proposed UFed-GAN. Our experimentalresults demonstrate the strong potential of UFed-GAN in addressing limitedcomputational resources and unlabeled data while preserving privacy.

UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data