Summary of Federated Continual Learning For Edge-ai: a Comprehensive Survey, by Zi Wang et al.
Federated Continual Learning for Edge-AI: A Comprehensive Survey
by Zi Wang, Fei Wu, Feng Yu, Yurui Zhou, Jia Hu, Geyong Min
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI)
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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 This survey provides a comprehensive review of federated continual learning (FCL) for edge AI. FCL enables the deployment of advanced AI models at the network edge while preserving data privacy and retaining knowledge from previous tasks as it learns new ones. The paper categorizes FCL methods based on three task characteristics: class, domain, and task continual learning. Each category is reviewed in-depth, covering background, challenges, problem formalisation, solutions, and limitations. The survey also reviews existing real-world applications empowered by FCL, indicating the current progress and potential of FCL in diverse application domains. Finally, the paper discusses several prospective research directions for FCL, including algorithm-hardware co-design and FCL with foundation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated continual learning (FCL) is a way to use AI at the edge of computer networks, where data is private and AI models can learn new things while keeping what they already know. This paper looks at how FCL works and how it’s used in different situations. It groups FCL methods into three types: class, domain, and task continual learning. Each group has its own challenges and solutions. The paper also talks about real-world applications of FCL and where it could go next. |
Keywords
» Artificial intelligence » Continual learning
