Summary of Unleashing the Power Of Continual Learning on Non-centralized Devices: a Survey, by Yichen Li et al.
Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
by Yichen Li, Haozhao Wang, Wenchao Xu, Tianzhe Xiao, Hong Liu, Minzhu Tu, Yuying Wang, Xin Yang, Rui Zhang, Shui Yu, Song Guo, Ruixuan Li
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 In this survey, we examine the development of non-centralized continual learning (NCCL) algorithms for distributed devices handling streaming data from a joint non-stationary environment. The goal is to conquer spatial and temporal dimension challenges such as distribution shifts, catastrophic forgetting, heterogeneity, and privacy issues. We review existing solutions from three levels to alleviate catastrophic forgetting and distribution shift. Additionally, we discuss various types of heterogeneity issues, security, and privacy attributes, as well as real-world applications across three prevalent scenarios. The survey also establishes a large-scale benchmark to analyze the performance of state-of-the-art NCCL approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how devices like vehicles and servers can learn from data streams together in a changing environment. It’s important to make sure these devices don’t forget what they’ve learned or get confused by new information. The paper looks at different ways to solve this problem, including making algorithms that work well in distributed systems and finding solutions for different types of heterogeneity. It also talks about real-world applications like security and privacy. |
Keywords
» Artificial intelligence » Continual learning
