# R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models

arXiv: https://arxiv.org/abs/2407.11654 · HTML: https://mlsanity.com/paper/2407.11654/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 70 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 30 of 35)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Why gradient clipping accelerates training: A theoretical justification for adaptivity](https://mlsanity.com/paper/1905.11881/) (2019)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models](https://mlsanity.com/paper/2103.15543/) (2021)
- [NEFTune: Noisy Embeddings Improve Instruction Finetuning](https://mlsanity.com/paper/2310.05914/) (2023)
- [Robustness to Unbounded Smoothness of Generalized SignSGD](https://mlsanity.com/paper/2208.11195/) (2022)
- [Training Robust Deep Neural Networks via Adversarial Noise Propagation](https://mlsanity.com/paper/1909.09034/) (2019)
- [Wireless Communications for Collaborative Federated Learning](https://mlsanity.com/paper/2006.02499/) (2020)
- [Gradient-Leakage Resilient Federated Learning](https://mlsanity.com/paper/2107.01154/) (2021)
- [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024)
- [Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences](https://mlsanity.com/paper/2305.00135/) (2023)
- [Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling](https://mlsanity.com/paper/2204.14017/) (2022)
- [The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications](https://mlsanity.com/paper/2405.01816/) (2024)
- [Jamming Attacks on Federated Learning in Wireless Networks](https://mlsanity.com/paper/2201.05172/) (2022)
- [Mitigating Smart Jammers in Multi-User MIMO](https://mlsanity.com/paper/2208.01453/) (2022)

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

- [Intelligent Orchestration of Distributed Large Foundation Model Inference at the Edge](https://mlsanity.com/paper/2504.03668/) (2025)
- [Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI](https://mlsanity.com/paper/2512.01039/) (2025)
- [CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks](https://mlsanity.com/paper/2509.19855/) (2025)
- [R-MTLLMF: Resilient Multi-Task Large Language Model Fusion at the Wireless Edge](https://mlsanity.com/paper/2411.18220/) (2024)
- [When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation](https://mlsanity.com/paper/2608.15639/) (2026)

## Abstract (arXiv)

Split federated learning (SFL) is a compute-efficient paradigm in distributed machine learning (ML), where components of large ML models are outsourced to remote servers. A significant challenge in SFL, particularly when deployed over wireless channels, is the susceptibility of transmitted model parameters to adversarial jamming that could jeopardize the learning process. This is particularly pronounced for embedding parameters in large language models (LLMs) and vision language models (VLMs), which are learned feature vectors essential for domain understanding. In this paper, rigorous insights are provided into the influence of jamming embeddings in SFL by deriving an expression for the ML training loss divergence and showing that it is upper-bounded by the mean squared error (MSE). Based on this analysis, a physical layer framework is developed for resilient SFL with LLMs (R-SFLLM) over wireless networks. R-SFLLM leverages wireless sensing data to gather information on the jamming directions-of-arrival (DoAs) for the purpose of devising a novel, sensing-assisted anti-jamming strategy while jointly optimizing beamforming, user scheduling, and resource allocation. Extensive experiments using both LLMs and VLMs demonstrate R-SFLLM's effectiveness, achieving close-to-baseline performance across various natural language processing (NLP) and computer vision (CV) tasks, datasets, and modalities. The proposed methodology further introduces an adversarial training component, where controlled noise exposure significantly enhances the model's resilience to perturbed parameters during training. The results show that more noise-sensitive models, such as RoBERTa, benefit from this feature, especially when resource allocation is unfair. It is also shown that worst-case jamming in particular translates into worst-case model outcomes, thereby necessitating the need for jamming-resilient SFL protocols.