Summary of Fedrdma: Communication-efficient Cross-silo Federated Llm Via Chunked Rdma Transmission, by Zeling Zhang et al.
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission
by Zeling Zhang, Dongqi Cai, Yiran Zhang, Mengwei Xu, Shangguang Wang, Ao Zhou
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI)
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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 FedRDMA system integrates RDMA into the federated learning (FL) communication protocol, addressing the growing concern of communication overhead in large AI models. This medium-difficulty summary highlights the technical details, focusing on the optimization techniques and experimental results. The paper’s key contributions include a series of optimization methods to improve the efficiency and robustness of RDMA-based communication, as well as a real-world evaluation scenario demonstrating up to 3.8x speedup in communication efficiency compared to traditional TCP/IP-based FL systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed system aims to reduce communication overhead in federated learning by using RDMA technology. This means breaking down the updated model into smaller chunks and making adjustments to make it work better over long distances. The system is tested on a real-world scenario and shows significant improvements, making it potentially very useful for large AI models. |
Keywords
* Artificial intelligence * Federated learning * Optimization
