Summary of Dynamicfl: Federated Learning with Dynamic Communication Resource Allocation, by Qi Le et al.
DynamicFL: Federated Learning with Dynamic Communication Resource Allocation
by Qi Le, Enmao Diao, Xinran Wang, Vahid Tarokh, Jie Ding, Ali Anwar
First submitted to arxiv on: 8 Sep 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 A new Federated Learning (FL) framework called DynamicFL is introduced, which tackles the issue of suboptimal model performance due to statistical heterogeneity in local data across devices. This framework investigates the trade-offs between global model performance and communication costs for two widely adopted FL methods: Federated Stochastic Gradient Descent (FedSGD) and Federated Averaging (FedAvg). DynamicFL allocates diverse communication resources to clients based on their data statistical heterogeneity, considering communication resource constraints. The method bridges the gap between FedSGD and FedAvg, providing a flexible framework that leverages communication heterogeneity to address statistical heterogeneity in FL. Through extensive experiments, DynamicFL is shown to surpass current state-of-the-art methods with up to a 10% increase in model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning helps people train models together using their local data. But when different devices have very different data, the model doesn’t perform well. A new way of doing Federated Learning called DynamicFL tries to fix this problem. It looks at how much communication each device needs based on its data and gives them more or less depending on that. This helps the model work better. The researchers tested it and found that it works really well, making the model up to 10% more accurate. |
Keywords
» Artificial intelligence » Federated learning » Stochastic gradient descent
