FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission

• URL: http://arxiv.org/abs/2403.00881v1
• Date: Fri, 1 Mar 2024 09:14:10 GMT
• Title: FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission
• Authors: Zeling Zhang, Dongqi Cai, Yiran Zhang, Mengwei Xu, Shangguang Wang, Ao Zhou
• Abstract summary: FedRDMA is a communication-efficient cross-silo FL system that integrates RDMA into the FL communication protocol. We show that sys can achieve up to 3.8$times$ speedup in communication efficiency compared to traditional TCP/IP-based FL systems.
• Score: 5.199151525305899
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models. In this paper, we propose FedRDMA, a communication-efficient cross-silo FL system that integrates RDMA into the FL communication protocol. To overcome the limitations of RDMA in wide-area networks (WANs), FedRDMA divides the updated model into chunks and designs a series of optimization techniques to improve the efficiency and robustness of RDMA-based communication. We implement FedRDMA atop the industrial federated learning framework and evaluate it on a real-world cross-silo FL scenario. The experimental results show that \sys can achieve up to 3.8$\times$ speedup in communication efficiency compared to traditional TCP/IP-based FL systems.

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