Imagine training a massive language model like BERT, not on a single powerful server, but spread across multiple devices, all connected wirelessly. This is the promise of split federated learning (SFL), a technique that boosts efficiency and privacy. But what happens when a malicious jammer enters the picture, trying to disrupt the model's training by injecting noise into the wireless transmissions? New research explores this vulnerability, particularly focusing on the impact of jamming on sensitive word embeddings, the building blocks of language understanding in LLMs. The results are concerning: even a single corrupted word embedding can subtly manipulate the model, and in a federated setting, this poisoning effect can quickly spread, rendering the global model useless. The paper introduces R-SFLLM, a clever framework that utilizes wireless sensing data to detect the direction of the jamming signal. Using this information, R-SFLLM dynamically adjusts beamforming, user scheduling, and resource allocation to mitigate the jammer's impact. The research demonstrates R-SFLLM's effectiveness in protecting the training process, enabling near-baseline performance even under aggressive jamming attacks. Interestingly, the study also reveals a surprising side effect: exposing LLMs to controlled noise during training, similar to what happens with jamming, can actually enhance their robustness. It's like giving the model a vaccine, teaching it to learn effectively even in the presence of interference. This opens exciting avenues for future research into adversarial training techniques. While R-SFLLM shows great promise, challenges remain. Ensuring fair resource allocation among all participating devices is crucial, as uneven distribution of resources can hinder the effectiveness of the protection, especially for more noise-sensitive models like RoBERTa. This work highlights the growing importance of security in the world of distributed AI. As we move towards a future where AI models are trained collaboratively across countless devices, safeguarding these training processes from malicious attacks will be paramount.