Summary of Fedah: Aggregated Head For Personalized Federated Learning, by Pengzhan Zhou et al.
FedAH: Aggregated Head for Personalized Federated Learning
by Pengzhan Zhou, Yuepeng He, Yijun Zhai, Kaixin Gao, Chao Chen, Zhida Qin, Chong Zhang, Songtao Guo
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel method called Federated Learning with Aggregated Head (FedAH) to address the limitations of Personalized-head-based PFL in retaining global knowledge. FedAH initializes the head with an aggregated head at each iteration and performs element-level aggregation between local and global model heads. This approach enables the personalized model to learn from both local and global knowledge, achieving better test accuracy by 2.87% compared to ten state-of-the-art FL methods. The effectiveness of FedAH is evaluated on five benchmark datasets in computer vision and natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedAH is a new way to improve Personalized Federated Learning (PFL). It helps the model learn from both local and global knowledge, making it better at recognizing patterns. This is important because PFL models are currently not very good at learning this kind of information. The researchers tested FedAH on several datasets and found that it did much better than other similar methods. |
Keywords
* Artificial intelligence * Federated learning * Natural language processing
