Summary of Algorithm Design For Continual Learning in Iot Networks, by Shugang Hao and Lingjie Duan
Algorithm Design for Continual Learning in IoT Networks
by Shugang Hao, Lingjie Duan
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); 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 The paper introduces a novel online learning technique called Continual Learning (CL) that aims to minimize forgetting loss on previously learned tasks when faced with sequentially generated streaming data from different tasks. The existing work focuses on reducing forgetting loss under a given task sequence, but this approach is not effective in real-world scenarios where similar tasks appear continuously. To address this issue, the authors propose an optimization problem and prove it NP-hard. They then develop a polynomial-time algorithm that achieves approximation ratios of 3/2 for underparameterized cases and 3/2 + r^(1-T) for overparameterized cases. The simulation results demonstrate the close-to-optimum performance of their proposed algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about how to learn new things without forgetting old ones when we’re presented with a stream of tasks that are similar but not identical. Imagine an autonomous vehicle learning different skills as it drives around. To our knowledge, no one has tackled this problem before, so the authors came up with a way to solve it. They turned it into a math problem and showed that their solution is good enough. |
Keywords
» Artificial intelligence » Continual learning » Online learning » Optimization
