  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 word embedding parameters in large language models
(LLMs), which are crucial for language understanding. In this paper, rigorous
insights are provided into the influence of jamming LLM word 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 BERT and RoBERTa models demonstrate R-SFLLM's effectiveness,
achieving close-to-baseline performance across various natural language
processing (NLP) tasks and datasets. The proposed methodology further
introduces an adversarial training component, where controlled noise exposure
significantly enhances the LLM'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.
