Summary of Towards Personalized Federated Learning Via Comprehensive Knowledge Distillation, by Pengju Wang and Bochao Liu and Weijia Guo and Yong Li and Shiming Ge
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation
by Pengju Wang, Bochao Liu, Weijia Guo, Yong Li, Shiming Ge
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 Federated learning is a distributed machine learning approach designed for data privacy preservation. However, heterogeneous client data leads to catastrophic forgetting, where models rapidly forget previous knowledge while acquiring new information. To address this issue, personalized federated learning has emerged to customize unique models for each client. Nevertheless, the inherent limitation of this mechanism lies in its excessive focus on personalization, potentially hindering model generalization. This paper presents a novel method that utilizes global and historical models as teachers and the local model as the student for comprehensive knowledge distillation. The historical model represents previous personalized knowledge, while the global model contains generalized knowledge from aggregated server models. By applying knowledge distillation, our method effectively transfers knowledge between models, mitigating catastrophic forgetting and enhancing overall performance of personalized models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to learn together without sharing your personal data with others. This is what federated learning does! However, when people have different information, the model can quickly forget what it learned before. To fix this problem, scientists created personalized federated learning, which makes a special model for each person. But this approach has its own limitation: it focuses too much on individual models and not enough on making sure they work well together. This paper proposes a new way to learn that combines the strengths of different models. It uses older models as teachers and helps them share their knowledge with newer models. By doing so, it prevents the model from forgetting what it learned before and makes it better at generalizing information. The results show that this approach is much more effective than existing methods. |
Keywords
» Artificial intelligence » Federated learning » Generalization » Knowledge distillation » Machine learning
