Presentation
Can Long-Haul RDMA Benefit Federated Learning?
DescriptionFederated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed training. However, its performance is often hindered by communication bottlenecks, especially over long-distance networks. In this work, we investigate the effectiveness of long-haul remote direct memory access (RDMA) as a high-performance communication substrate for FL. We develop a simulation framework that incorporates rate-limiting techniques to emulate wide-area RDMA deployments, enabling accurate comparisons with traditional TCP/IP networks. Through evaluations we demonstrate that long-haul RDMA can reduce communication time by up to 90.79% under WAN-like conditions and decrease total runtime by as much as 85.83%. These results underscore RDMA's promise in accelerating FL across distributed geographic settings.
Event Type
Research and ACM SRC Posters
TimeTuesday, 18 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
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