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

arXiv: https://arxiv.org/abs/2403.00881 · HTML: https://mlsanity.com/paper/2403.00881/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [Leveraging Large Language Models for Multiple Choice Question Answering](https://mlsanity.com/paper/2210.12353/) (2022)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models](https://mlsanity.com/paper/2212.10846/) (2022)
- [FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning](https://mlsanity.com/paper/2208.12268/) (2022)
- [Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes](https://mlsanity.com/paper/2312.06353/) (2023)
- [Cross-Silo Federated Learning: Challenges and Opportunities](https://mlsanity.com/paper/2206.12949/) (2022)
- [Integration of Large Language Models and Federated Learning](https://mlsanity.com/paper/2307.08925/) (2023)
- [Low-Parameter Federated Learning with Large Language Models](https://mlsanity.com/paper/2307.13896/) (2023)
- [Natural Language Processing for EHR-Based Computational Phenotyping](https://mlsanity.com/paper/1806.04820/) (2018)
- [Federated Few-Shot Learning for Mobile NLP](https://mlsanity.com/paper/2212.05974/) (2022)
- [A Federated Framework for LLM-based Recommendation](https://mlsanity.com/paper/2402.09959/) (2024)

## Built on by (most cited first, 5 of 5)

- [Federated Large Language Models: Current Progress and Future Directions](https://mlsanity.com/paper/2409.15723/) (2024)
- [Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach](https://mlsanity.com/paper/2604.21197/) (2026)
- [ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data](https://mlsanity.com/paper/2603.00516/) (2026)
- [Enhancing Model Privacy in Federated Learning with Random Masking and Quantization](https://mlsanity.com/paper/2508.18911/) (2025)
- [Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients](https://mlsanity.com/paper/2505.06335/) (2025)

## Abstract (arXiv)

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× speedup in communication efficiency compared to traditional TCP/IP-based FL systems.