The field of federated learning is moving towards more autonomous and efficient systems, with a focus on leveraging large language models (LLMs) to improve performance and reduce communication overhead. Recent developments have highlighted the potential of LLMs as universal feature extractors, enabling the alignment of heterogeneous client data and improving the accuracy of federated learning models. Additionally, advancements in Low-Rank Adaptation (LoRA) fine-tuning have led to more efficient and effective methods for adapting LLMs to specific tasks and domains. Noteworthy papers in this area include: FedAgentBench, which introduces an agent-driven FL framework for automating real-world federated medical image analysis. Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs, which proposes an asymmetric multi-LoRA design for more balanced performance across tasks. Commmunication-Efficient and Accurate Approach for Aggregation in Federated Low-Rank Adaptation, which achieves state-of-the-art global performance while maintaining low communication overhead. Federated Learning Meets LLMs: Feature Extraction From Heterogeneous Clients, which leverages pre-trained LLMs as universal feature extractors for federated learning. LoRAFusion, which introduces an efficient LoRA fine-tuning system for LLMs, achieving up to 1.96x end-to-end speedup compared to existing systems. Flow of Knowledge: Federated Fine-Tuning of LLMs in Healthcare under Non-IID Conditions, which presents a federated fine-tuning approach based on LoRA for privacy-preserving knowledge flow across institutions. Family Matters: Language Transfer and Merging for Adapting Small LLMs to Faroese, which investigates how to adapt small LLMs to low-resource languages via transfer and merging.