Summary of Fedfa: a Fully Asynchronous Training Paradigm For Federated Learning, by Haotian Xu et al.
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
by Haotian Xu, Zhaorui Zhang, Sheng Di, Benben Liu, Khalid Ayed Alharthi, Jiannong Cao
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel approach to federated learning, which enables decentralized machine learning model training on multiple devices while ensuring data privacy. The FedAvg algorithm is a widely used parameter update strategy that aims to eliminate heterogeneous data effects and ensure convergence. However, this approach suffers from significant waiting time costs due to the synchronization requirements for each communication round. To address this issue, recent solutions have introduced semi-asynchronous approaches that guarantee convergence while reducing waiting times. Despite these advancements, there is still a need to completely eliminate waiting times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for many devices to work together on machine learning tasks without sharing their private data. The FedAvg algorithm helps make sure the results are consistent and good, but it takes a long time because devices have to wait for each other. Researchers have been trying to find ways to speed up this process while keeping the results accurate. They’ve come up with some new ideas that work well, but there’s still room for improvement. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
