Summary of Fppl: An Efficient and Non-iid Robust Federated Continual Learning Framework, by Yuchen He et al.
FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework
by Yuchen He, Chuyun Shen, Xiangfeng Wang, Bo Jin
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 continual learning (FCL) aims to learn from sequential data streams in decentralized settings, addressing catastrophic forgetting. Existing FCL methods rely on rehearsal mechanisms, which can compromise privacy or require additional storage and computation. This work introduces Federated Prototype-Augmented Prompt Learning (FPPL), a framework that learns lightweight prompts augmented by prototypes without rehearsal. FPPL employs fusion functions to leverage task-specific prompts for alleviating catastrophic forgetting. On the server side, contrastive learning uses global prototypes aggregated from clients to mitigate non-IID-derived data heterogeneity. Debiased classifiers are trained on locally uploaded prototypes to alleviate performance degradation caused by both non-IID and catastrophic forgetting. Empirical evaluations demonstrate FPPL’s effectiveness, achieving notable performance with an efficient design while remaining robust to diverse non-IID degrees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way for computers to learn from lots of data without losing information over time. This can be important because the world is generating so much data that it’s hard to keep track of everything. Right now, there are ways for computers to learn from this data, but they have some problems. Some methods require sharing too much information or using a lot of extra storage and computing power. The new method, called FPPL, solves these problems by learning in a way that is more efficient and doesn’t compromise privacy. This makes it easier for computers to keep learning from the data without forgetting important things. |
Keywords
» Artificial intelligence » Continual learning » Prompt
