International Conference on Computer and Knowledge Engineering

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SGFL: A Federated Learning Approach for Non-IID Data Using Semi-Supervised DCGAN

Authors :

Alireza Rabiee¹ Abolfazl Ajdarloo² Mohsen Rahmani³

1- Computer Engineering Group, Faculty of Engineering, Arak University 2- Computer Engineering Group, Faculty of Engineering, Arak University 3- Computer Engineering Group, Faculty of Engineering, Arak University

Keywords :

Federated Learning،Non-IID Data،Generative Adversarial Network (GAN)،Deep Convolutional Generative Adversarial Network (DCGAN)،Semi-Supervised DCGAN

Abstract :

Federated learning (FL) has emerged as a method for collaborative training machine learning models on decentralized machines. However, the assumption of independently and identically distributed (IID) data is often not true in real-world scenarios because the data distribution can vary significantly from device to device. This paper presents SGFL, a novel Federated Learning approach designed specifically to address the challenges posed by non-IID data distributions. SGFL employs the power of semi-supervised learning and deep convolutional generative adversarial networks (DCGANs) to enhance the federated learning process. By leveraging the redundant data received from each device, SGFL utilizes a semi-supervised DCGAN to fine-tune a global model. This approach enables better modeling of non-IID data while preserving privacy and security through local training. We evaluate the performance of SGFL on a non-IID datasets, and the results demonstrate its effectiveness in achieving higher accuracy compared to traditional federated learning methods. The source codes of the methods presented in the paper is available at https://github.com/apaliray03/SGFL.

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