Summary of Efficient Model Compression For Hierarchical Federated Learning, by Xi Zhu et al.
Efficient Model Compression for Hierarchical Federated Learning
by Xi Zhu, Songcan Yu, Junbo Wang, Qinglin Yang
First submitted to arxiv on: 27 May 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 In this paper, researchers introduce a new hierarchical federated learning framework that addresses communication efficiency in distributed learning systems. The framework combines clustered federated learning and model compression to preserve privacy while reducing energy consumption. The proposed algorithm uses an adaptive clustering technique to identify core clients and dynamically organize clients into clusters, which enhances transmission efficiency by implementing local aggregation with compression (LC aggregation) at each core client. This approach maintains comparable predictive accuracy while significantly reducing energy consumption compared to existing federated learning mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper presents a new way for devices to learn together without sharing their private data. It’s like a team effort where devices work together to improve their models, but they don’t have to share all the details. The new approach reduces energy consumption and makes it more efficient. This is important because as more devices are connected to the internet, we need ways to make sure our personal information stays safe while still allowing us to benefit from learning together. |
Keywords
» Artificial intelligence » Clustering » Federated learning » Model compression
