Summary of Dualfed: Enjoying Both Generalization and Personalization in Federated Learning Via Hierachical Representations, by Guogang Zhu et al.
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
by Guogang Zhu, Xuefeng Liu, Jianwei Niu, Shaojie Tang, Xinghao Wu, Jiayuan Zhang
First submitted to arxiv on: 25 Jul 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 A new approach to personalized federated learning (PFL) called DualFed is proposed, which achieves both high model generalization and effective personalization simultaneously. This is challenging because existing PFL methods can only manage a trade-off between these two objectives. The key idea is that deep models exhibit hierarchical architectures, which produce representations with various levels of generalization and personalization at different stages. By selecting multiple representations from these layers and combining them, it is possible to achieve both goals concurrently. However, this method is infeasible due to high computational costs. To address this problem, DualFed inserts a personalized projection network between the encoder and classifier, allowing for the capture of generalized information shareable across clients and task-specific information on local clients. This design minimizes mutual interference between generalization and personalization, achieving a win-win situation. Experimental results show that DualFed outperforms other FL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Personalized federated learning (PFL) is a way to train artificial intelligence models that work well for individual users while also being good at general tasks. Right now, most PFL approaches have to choose between doing one or the other really well. This new approach called DualFed can do both at the same time! It does this by looking at how deep learning models are structured and using different parts of those models to capture information that is useful for both generalization and personalization. By doing things this way, DualFed avoids a major problem with previous approaches, which made them too hard to compute. The results show that DualFed works better than other PFL methods. |
Keywords
» Artificial intelligence » Deep learning » Encoder » Federated learning » Generalization
