Summary of Hydra-fl: Hybrid Knowledge Distillation For Robust and Accurate Federated Learning, by Momin Ahmad Khan et al.
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning
by Momin Ahmad Khan, Yasra Chandio, Fatima Muhammad Anwar
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 innovative approaches to address the issue of data heterogeneity in Federated Learning (FL) users, a problem that can lead to reduced global model performance. The researchers explore Knowledge Distillation (KD)-based methods as potential solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data from different sources can’t agree on what’s best for a shared machine learning model. This “data heterogeneity” is a big challenge in Federated Learning (FL), making the model perform worse than expected. Scientists have tried various ways to fix this, including something called Knowledge Distillation (KD). |
Keywords
» Artificial intelligence » Federated learning » Knowledge distillation » Machine learning
