Summary of Fedcada: Adaptive Client-side Optimization For Accelerated and Stable Federated Learning, by Liuzhi Zhou et al.
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
by Liuzhi Zhou, Yu He, Kun Zhai, Xiang Liu, Sen Liu, Xingjun Ma, Guangnan Ye, Yu-Gang Jiang, Hongfeng Chai
First submitted to arxiv on: 20 May 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 proposes an innovative approach called FedCAda, a federated client adaptive algorithm designed to balance acceleration and stability in collaborative machine learning training while preserving data privacy. FedCAda leverages the Adam algorithm to adjust the correction process of moment estimates on the client-side and aggregate adaptive parameters on the server-side, aiming to accelerate convergence speed and communication efficiency while ensuring stability and performance. The authors investigate various adjustment functions and demonstrate that due to limited information in initial stages, more constraints are needed, which diminish as federated learning progresses. Experimental results on CV and NLP datasets show that FedCAda outperforms state-of-the-art methods in terms of adaptability, convergence, stability, and overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedCAda is a new way to train machine learning models together with other computers while keeping their data private. The problem is that this process can be slow or unstable if not done correctly. The authors created an algorithm called FedCAda that helps fix this issue by adjusting how the computers learn from each other. They tested it on different types of data and found that it works better than other methods. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Nlp
