Summary of Generative Ai Like Chatgpt in Blockchain Federated Learning: Use Cases, Opportunities and Future, by Sai Puppala et al.
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
by Sai Puppala, Ismail Hossain, Md Jahangir Alam, Sajedul Talukder, Jannatul Ferdaus, Mahedi Hasan, Sameera Pisupati, Shanmukh Mathukumilli
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Federated learning has emerged as a prominent approach for training machine learning models using decentralized data without requiring the sharing of this data. The integration of generative artificial intelligence (AI) methods offers new possibilities for enhancing privacy, augmenting data, and customizing models. This research explores potential integrations of generative AI in federated learning, revealing opportunities to boost privacy, data efficiency, and model performance. Generative models like generative adversarial networks (GANs) and variational autoencoders (VAEs) are particularly emphasized for creating synthetic data that replicates the distribution of real data. Generating synthetic data helps 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to train machine learning models without sharing private data. Recently, adding artificial intelligence (AI) methods has helped make this process better. This paper looks at how these AI methods can be used together with federated learning to improve things like privacy, using less data, and making more accurate models. The main idea is to create fake data that looks like real data, using tools like generative adversarial networks (GANs) and variational autoencoders (VAEs). This helps solve problems when there isn’t enough real data. It also makes it possible to develop better models. This paper shows how AI can be used in federated learning to make more personalized solutions. |
Keywords
* Artificial intelligence * Federated learning * Machine learning * Synthetic data
