Summary of Fedlion: Faster Adaptive Federated Optimization with Fewer Communication, by Zhiwei Tang et al.
FedLion: Faster Adaptive Federated Optimization with Fewer Communication
by Zhiwei Tang, Tsung-Hui Chang
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 research proposes Federated Learning (FL) algorithm, called FedLion, to improve training speed and reduce communication costs in distributed machine learning model development. By incorporating elements from the centralized adaptive algorithm Lion, FedLion adapts to changing data distributions and outperforms previous state-of-the-art algorithms like FAFED and FedDA on widely adopted FL benchmarks. Additionally, signed gradients in local training reduce uplink transmission requirements, leading to further communication cost savings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedLion is a new way to train machine learning models when the data is spread across different devices or locations. The algorithm helps speed up the training process by adjusting how it works based on the data it sees. This makes it better than other algorithms that do similar things. The researchers tested FedLion and found it did even better than some of the best current methods. They also looked at why this happens and found that using special kinds of gradients helps reduce the need for devices to send each other information, which saves time and resources. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
