Summary of Sketched Adaptive Federated Deep Learning: a Sharp Convergence Analysis, by Zhijie Chen et al.
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis
by Zhijie Chen, Qiaobo Li, Arindam Banerjee
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Combining gradient compression methods (e.g., CountSketch, quantization) and adaptive optimizers (e.g., Adam, AMSGrad) is a desirable goal in federated learning (FL), with potential benefits on both fewer communication rounds and less per-round communication. The authors introduce specific sketched adaptive federated learning (SAFL) algorithms and provide theoretical convergence analyses in different FL settings with guarantees on communication cost depending only logarithmically (instead of linearly) on the ambient dimension. This allows for faster convergence and improved efficiency. The authors show that SAFL achieves asymptotic O(1/√T) convergence, and converges faster in the initial epochs. In the non-i.i.d. client setting, where non-adaptive methods lack convergence guarantees, SAFL algorithms can provably converge despite additional heavy-tailed noise. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning (FL) is a way for devices to learn together without sharing their data. It’s like a big team working together to get better at a task. The problem is that it uses a lot of communication, which takes time and energy. To solve this, the authors combined two ideas: making gradients smaller and using special optimizers. They created new algorithms called SAFL (Sketched Adaptive Federated Learning) and showed they work well in different situations. This means FL can be faster and more efficient. |
Keywords
* Artificial intelligence * Federated learning * Quantization