Summary of Fed-cvlc: Compressing Federated Learning Communications with Variable-length Codes, by Xiaoxin Su et al.
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
by Xiaoxin Su, Yipeng Zhou, Laizhong Cui, John C.S. Lui, Jiangchuan Liu
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this research paper, the authors propose a novel approach to Federated Learning (FL) that addresses the communication bottleneck between the parameter server and distributed clients. The current methods for model compression in FL assume a fixed code length, which does not account for the heterogeneity of model updates. To overcome this limitation, the authors introduce Fed-CVLC, a compression technique that fine-tunes the code length based on the dynamics of model updates. They develop an optimal tuning strategy to minimize the loss function while adhering to communication budget constraints. The results demonstrate that Fed-CVLC outperforms state-of-the-art baselines, achieving improved model utility and reduced communication traffic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to do Federated Learning, which helps people keep their personal data private when sharing models with others. Right now, there’s a problem with how we compress these models to send them quickly over the internet. The authors came up with a better idea called Fed-CVLC that adjusts the way it sends the model updates based on what’s happening. They tested this new method and found it works much better than other approaches, making it faster and more useful. |
Keywords
* Artificial intelligence * Federated learning * Loss function * Model compression
