Summary of Client-supervised Federated Learning: Towards One-model-for-all Personalization, by Peng Yan et al.
Client-supervised Federated Learning: Towards One-model-for-all Personalization
by Peng Yan, Guodong Long
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Personalized Federated Learning (PerFL) paradigm delivers personalized models for diverse clients under federated learning settings, requiring extra learning processes on a client to adapt a globally shared model to the client-specific personalized model using its own local data. However, this model adaptation process remains an open challenge in the stage of model deployment and test time. The authors tackle this challenge by proposing a novel federated learning framework that learns only one robust global model achieving competitive performance to those personalized models on unseen/test clients. They design Client-Supervised Federated Learning (FedCS) to unravel clients’ bias on instances’ latent representations, enabling the global model to learn both client-specific and client-agnostic knowledge. Experimental results demonstrate that FedCS can learn a robust FL global model for changing data distributions of unseen/test clients. The proposed framework’s global model can be directly deployed to test clients while achieving comparable performance to other personalized FL methods requiring model adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to teach machines how to work together is being explored, called Personalized Federated Learning (PerFL). Right now, when we train these machines, we need to adapt the learning process for each individual client. This can be time-consuming and costly. The authors of this paper propose a new approach that allows us to learn just one model that works well for all clients, without needing to adapt it for each one. They call this method Client-Supervised Federated Learning (FedCS). It helps the machines understand the differences between individual clients and learn from them. This new way of learning has been tested and shown to be effective in a variety of situations. |
Keywords
* Artificial intelligence * Federated learning * Supervised
