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

TL;DR

UFed-GAN is a novel federated learning framework that enables privacy-preserving, efficient data distribution modeling in resource-constrained, unlabeled environments without local classification training.

Contribution

The paper introduces UFed-GAN, a new unsupervised federated GAN framework designed for low-resource, unlabeled data scenarios in privacy-sensitive applications.

Findings

UFed-GAN effectively models data distribution without local labels.

The framework maintains privacy while operating under limited computational resources.

Experimental results show UFed-GAN's potential in real-world applications.

Abstract

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.