Summary of Effective Heterogeneous Federated Learning Via Efficient Hypernetwork-based Weight Generation, by Yujin Shin et al.
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation
by Yujin Shin, Kichang Lee, Sungmin Lee, You Rim Choi, Hyung-Sin Kim, JeongGil Ko
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed HypeMeFed framework tackles the challenges of federated learning on heterogeneous devices by combining multi-exit network architecture with hypernetwork-based model weight generation, addressing feature space disparities during aggregation. The approach minimizes computation and memory overhead through low-rank factorization, achieving 5.12% accuracy boost over FedAvg, reducing hypernetwork memory requirements by 98.22%, and accelerating operations by 1.86x. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HypeMeFed is a new way to make sure devices with different abilities can work together for federated learning. This is important because some devices have more power or memory than others. The system uses special networks that help connect these devices and makes it easier to combine their information. It also makes the process faster and uses less space on each device. This means HypeMeFed can help make AI better and more accessible. |
Keywords
* Artificial intelligence * Federated learning
