Summary of Federated Learning with New Knowledge: Fundamentals, Advances, and Futures, by Lixu Wang et al.
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
by Lixu Wang, Yang Zhao, Jiahua Dong, Ating Yin, Qinbin Li, Xiao Wang, Dusit Niyato, Qi Zhu
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 (FL) is rapidly developing as a privacy-preserving distributed learning approach, driven by the growing importance of privacy protection. To address this trend and emerging demands for FL in real-world applications, we focus on Federated Learning with New Knowledge. Our primary challenge is to effectively integrate various new knowledge into existing FL systems, reducing costs, extending lifespan, and facilitating sustainable development. We systematically define sources of new knowledge in FL, including new features, tasks, models, and algorithms. For each source, we analyze how to incorporate new knowledge into existing FL systems, examining the impact of new knowledge arrival on the incorporation process. We also discuss potential future directions for FL with new knowledge, considering scenario setups, efficiency, and security. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way to learn from lots of devices without sharing private data. It’s getting more popular because people care about privacy. Imagine you want to improve this technology to make it better and longer-lasting. That’s what we’re doing in this paper. We’re looking at how to add new information, like new features or tasks, into existing Federated Learning systems. We’re also thinking about the best ways to do this and what might happen if we get new information at different times. Finally, we’re talking about where Federated Learning with new knowledge might go in the future. |
Keywords
* Artificial intelligence * Federated learning
