Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future

TL;DR

This paper explores how generative AI techniques like GANs and VAEs can enhance federated learning by improving privacy, data augmentation, and personalization, opening new opportunities for decentralized machine learning.

Contribution

It provides a comprehensive analysis of integrating generative AI into federated learning, highlighting new use cases, opportunities, and future research directions.

Findings

Generative AI can improve privacy and data efficiency in federated learning.

Synthetic data generation helps address data scarcity issues.

Personalized federated learning benefits from generative models.

Abstract

Federated learning has become a significant approach for training machine learning models using decentralized data without necessitating the sharing of this data. Recently, the incorporation of generative artificial intelligence (AI) methods has provided new possibilities for improving privacy, augmenting data, and customizing models. This research explores potential integrations of generative AI in federated learning, revealing various opportunities to enhance privacy, data efficiency, and model performance. It particularly emphasizes the importance of generative models like generative adversarial networks (GANs) and variational autoencoders (VAEs) in creating synthetic data that replicates the distribution of real data. Generating synthetic data helps federated learning address challenges related to limited data availability and supports robust model development. Additionally, we examine various applications of generative AI in federated learning that enable more personalized solutions.