Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914888123678720 |
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| author | Puppala, Sai Hossain, Ismail Alam, Md Jahangir Talukder, Sajedul Ferdaus, Jannatul Hasan, Mahedi Pisupati, Sameera Mathukumilli, Shanmukh |
| author_facet | Puppala, Sai Hossain, Ismail Alam, Md Jahangir Talukder, Sajedul Ferdaus, Jannatul Hasan, Mahedi Pisupati, Sameera Mathukumilli, Shanmukh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18358 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future Puppala, Sai Hossain, Ismail Alam, Md Jahangir Talukder, Sajedul Ferdaus, Jannatul Hasan, Mahedi Pisupati, Sameera Mathukumilli, Shanmukh Machine Learning Artificial Intelligence Cryptography and Security Distributed, Parallel, and Cluster Computing 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. |
| title | Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2407.18358 |