Summary of Buffer-based Gradient Projection For Continual Federated Learning, by Shenghong Dai et al.
Buffer-based Gradient Projection for Continual Federated Learning
by Shenghong Dai, Jy-yong Sohn, Yicong Chen, S M Iftekharul Alam, Ravikumar Balakrishnan, Suman Banerjee, Nageen Himayat, Kangwook Lee
First submitted to arxiv on: 3 Sep 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 This paper introduces Fed-A-GEM, a federated adaptation of the A-GEM method for Continual Federated Learning (CFL). CFL enables real-world applications where multiple decentralized clients adaptively learn from continuous data streams. The challenge in CFL is mitigating catastrophic forgetting, where models lose previously acquired knowledge when learning new information. Existing approaches often face difficulties due to device storage constraints and heterogeneous data distributions among clients. Fed-A-GEM alleviates catastrophic forgetting by leveraging local buffer samples and aggregated buffer gradients, thus preserving knowledge across multiple clients. Our method combines with existing CFL techniques, enhancing their performance in the CFL context. Experiments on standard benchmarks show consistent performance improvements across diverse scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn from lots of data sent from many devices. This is useful for things like self-driving cars and personal assistants. Right now, machines have trouble remembering what they learned before when they learn new things. The authors came up with a way to help machines remember using something called “buffer-based gradient projection.” They tested this method on some big datasets and found that it made the machine learning models better. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
