FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission
<|reference_start|>FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission: 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.<|reference_end|>
@article{zhang2024fedrdma:,
    title={FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked
  RDMA Transmission},
    author={Zeling Zhang, Dongqi Cai, Yiran Zhang, Mengwei Xu, Shangguang Wang, Ao
  Zhou},
    journal={arXiv preprint arXiv:2403.00881},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00881},
    primaryClass={cs.LG cs.DC cs.NI}
}