Summary of Hyper-parameter Optimization For Federated Learning with Step-wise Adaptive Mechanism, by Yasaman Saadati and M. Hadi Amini
Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism
by Yasaman Saadati, M. Hadi Amini
First submitted to arxiv on: 19 Nov 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 paper investigates the integration of lightweight Hyper-Parameter Optimization (HPO) tools into Federated Learning (FL) settings to enhance the efficiency of Automated FL (Auto-FL). The authors leverage two HPO tools, Raytune and Optuna, within a step-wise feedback mechanism to accelerate the hyper-parameter tuning process. A novel client selection technique is also introduced to mitigate the straggler effect in Auto-FL. The paper evaluates the selected HPO tools using FEMNIST and CIFAR10 benchmark datasets, highlighting the importance of efficient HPO tools for successful FL deployments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way for machines to learn together without sharing their data. It’s important because it keeps people’s information private. Right now, making this work well is tricky because it needs lots of adjustments. Some people have tried using special software to help make these adjustments faster. This paper looks at two new tools that can be used with Federated Learning to make the process faster and better. The authors also came up with a way to pick which devices should do what to help everything work together smoothly. They tested their ideas on some benchmark datasets and found that it worked well. |
Keywords
* Artificial intelligence * Federated learning * Optimization
