Summary of Communication-efficient Federated Learning Via Clipped Uniform Quantization, by Zavareh Bozorgasl and Hao Chen
Communication-Efficient Federated Learning via Clipped Uniform Quantization
by Zavareh Bozorgasl, Hao Chen
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA); Signal Processing (eess.SP)
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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 The novel approach presented in this paper aims to enhance communication efficiency in federated learning by introducing clipped uniform quantization. The method uses optimal clipping thresholds and adaptive quantization schemes to reduce bandwidth and memory requirements, while maintaining competitive model accuracy. The authors investigate the effects of symmetric clipping and uniform quantization on model performance, highlighting the role of stochastic quantization in mitigating artifacts and improving robustness. Extensive simulations demonstrate that the method achieves near-full-precision performance with significant communication savings. Additionally, the proposed approach enables efficient weight averaging based on inverse mean squared quantization errors, balancing communication efficiency and model accuracy. Furthermore, this design ensures client privacy by not requiring disclosure of data volumes to the server. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper presents a new way to make machine learning work better over the internet. Right now, it can be slow and use a lot of memory because we’re sending lots of information back and forth. The new approach reduces this problem by compressing the information before sending it. This makes it faster and uses less memory. The results show that this method works well and is much more efficient than previous methods. Additionally, this design keeps client data private, which is important for security. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Precision » Quantization
