The field of edge AI is rapidly advancing with a focus on efficient and private processing of large language models. Researchers are exploring ways to reduce latency and communication overhead in bandwidth-constrained settings by leveraging federated learning and hybrid language models. Notable innovations include collaborative learning of uncertainty thresholds, hierarchical model aggregation, and privacy-aware fine-tuning methods. These advancements have the potential to transform edge deployment of large language models, enabling scalable and efficient applications. Noteworthy papers include Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission, which reduces LLM transmissions by over 95 percent with negligible accuracy loss. PAE MobiLLM introduces a privacy-aware and efficient LLM fine-tuning method via additive side-tuning, enabling secure and efficient fine-tuning on mobile devices.