Summary of Saddle: Sharpness-aware Decentralized Deep Learning with Heterogeneous Data, by Sakshi Choudhary et al.
SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous Data
by Sakshi Choudhary, Sai Aparna Aketi, Kaushik Roy
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)
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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 Medium Difficulty summary: Decentralized training enables learning from distributed datasets without a central server, but heterogeneous data distributions and high communication costs can lead to over-fitting and poor generalization. SADDLe is a set of decentralized deep learning algorithms that leverage Sharpness-Aware Minimization (SAM) to find a flatter loss landscape during training, resulting in better model generalization and robustness to compression. Two versions of the approach are presented, with experiments showing 1-20% improvement in test accuracy compared to existing techniques, and an average drop of only 1% with up to 4x compression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper talks about a new way to train artificial intelligence models when they’re connected to different locations. The problem is that the data from each location might be very different, which can make the model not work well overall. Also, it’s hard for these models to share information with each other because they need to communicate so much. To solve this, the paper proposes a new set of algorithms called SADDLe. These algorithms use an idea called Sharpness-Aware Minimization (SAM) to help the models learn better and be more robust to changes. The results show that SADDLe can improve test accuracy by 1-20% compared to other methods. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Sam
