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

Autor:	Puppala, Sai, Hossain, Ismail, Alam, Md Jahangir, Talukder, Sajedul, Ferdaus, Jannatul, Hasan, Mahedi, Pisupati, Sameera, Mathukumilli, Shanmukh
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Cryptography and Security Computer Science - Distributed Parallel and Cluster Computing
Druh dokumentu:	Working Paper
Popis:	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. Comment: We are going to submit this research article into a conference which is best fit for this topic
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.18358 Zobrazit plný text záznamu View this record from Arxiv