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.

FedRDMA 是一种在联邦学习通信协议中集成 RDMA 的通信高效型联邦学习系统，通过将更新的模型切分为块，并设计了一系列优化技术以提高基于 RDMA 的通信的效率和鲁棒性，实验结果显示，与传统的基于 TCP/IP 的联邦学习系统相比，FedRDMA 能够实现高达 3.8 倍的通信效率提升。