Summary of A Resource-adaptive Approach For Federated Learning Under Resource-constrained Environments, by Ruirui Zhang et al.
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments
by Ruirui Zhang, Xingze Wu, Yifei Zou, Zhenzhen Xie, Peng Li, Xiuzhen Cheng, Dongxiao Yu
First submitted to arxiv on: 19 Jun 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 Fed-RAA algorithm is a Resource-Adaptive Asynchronous Federated learning approach that addresses limitations in existing federated learning methods. The algorithm adaptively allocates fragments of the global model to clients based on their computing and communication capabilities, enabling heterogeneous clients to participate in training. Compared with vanilla FL methods, Fed-RAA improves fairness by allocating tasks according to client resources. Theoretical analysis confirms the convergence of our approach. Experimental results on MNIST, CIFAR-10, and CIFAR-100 demonstrate the advantages of Fed-RAA over baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in artificial intelligence called federated learning. Right now, it’s hard for lots of devices to work together to learn new things because they all have different resources like computer power or internet speed. This makes it difficult for them to share their knowledge with each other. The researchers created a new way to make this happen called Fed-RAA. It helps devices with different abilities work together by giving each one only the parts of the job that fit its capabilities. This makes it more fair and efficient. They tested this method on some big datasets and showed that it works better than older methods. |
Keywords
* Artificial intelligence * Federated learning
