Summary of Fedd2s: Personalized Data-free Federated Knowledge Distillation, by Kawa Atapour et al.
FedD2S: Personalized Data-Free Federated Knowledge Distillation
by Kawa Atapour, S. Jamal Seyedmohammadi, Jamshid Abouei, Arash Mohammadi, Konstantinos N. Plataniotis
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Image and Video Processing (eess.IV)
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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 This paper tackles the challenge of mitigating data heterogeneity among clients within a Federated Learning (FL) framework, addressing the issue of model drift that occurs when client data does not follow the same distribution as the global model. The proposed approach, FedD2S for Personalized Federated Learning (pFL), leverages knowledge distillation and incorporates a deep-to-shallow layer-dropping mechanism to enhance local model personalization. This is achieved through simulations on diverse image datasets such as FEMNIST, CIFAR10, CINIC0, and CIFAR100, comparing FedD2S with state-of-the-art FL baselines. The results show superior performance characterized by accelerated convergence and improved fairness among clients. The study also investigates the impact of key hyperparameters, providing valuable insights into the optimal configuration for FedD2S. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem in how machines learn together from different data sources. When these sources are very different, it’s hard to get good results. To fix this, the authors created a new way to learn called FedD2S. It uses an old idea called knowledge distillation and adds some new tricks to make it work better. They tested it on lots of pictures and found that it worked really well. The results were faster and more accurate than other ways of doing things. |
Keywords
* Artificial intelligence * Federated learning * Knowledge distillation
