UFed-GAN: A Secure Federated Learning Framework with Constrained
  Computation and Unlabeled Data
Achintha Wijesinghe Songyang Zhang Siyu Qi Zhi Ding
To satisfy the broad applications and insatiable hunger for deploying low
latency multimedia data classification and data privacy in a cloud-based
setting, federated learning (FL) has emerged as an important learning paradigm.
For the practical cases involving limited computational power and only
unlabeled data in many wireless communications applications, this work
investigates FL paradigm in a resource-constrained and label-missing
environment. Specifically, we propose a novel framework of UFed-GAN:
Unsupervised Federated Generative Adversarial Network, which can capture
user-side data distribution without local classification training. We also
analyze the convergence and privacy of the proposed UFed-GAN. Our experimental
results demonstrate the strong potential of UFed-GAN in addressing limited
computational resources and unlabeled data while preserving privacy.