Summary of A Robust Federated Learning Framework For Undependable Devices at Scale, by Shilong Wang et al.
A Robust Federated Learning Framework for Undependable Devices at Scale
by Shilong Wang, Jianchun Liu, Hongli Xu, Chunming Qiao, Huarong Deng, Qiuye Zheng, Jiantao Gong
First submitted to arxiv on: 28 Dec 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 The proposed federated learning framework, FLUDE, addresses the issue of unreliable devices in a federated learning system by assessing device dependability based on historical behavior and adapting to interruptible training. To mitigate resource waste, FLUDE maintains a model cache on each device and implements a staleness-aware strategy for distributing the global model. This approach is demonstrated to improve model performance and efficiency using physical platforms with 120 smartphones and NVIDIA Jetson devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps many devices like smartphones learn together without sharing their data. But when these devices are not always connected, it can be a problem. A new system called FLUDE makes sure devices that are likely to stay online for training get picked first. It also saves progress on each device so work isn’t wasted if something goes wrong. This helps make the learning process better and more efficient. |
Keywords
* Artificial intelligence * Federated learning
