This research paper proposes a new architecture for federated learning of large language models (LLMs) over mobile networks, addressing challenges in mobile RAN transport. The proposed approach uses in-network aggregation to reduce the number of transfers and enable selective provisioning of optical connectivity.

FedSocket is a recipient-executable knowledge exchange framework for heterogeneous multimodal federated learning. It enables recipient models to execute exchanged knowledge directly, improving accuracy and efficiency. FedSocket combines local and exchanged predictions to achieve better results than independent ensembles or local models alone.

FedLAFP proposes a framework for federated fine-tuning of pre-trained models, improving personalized prediction by using a combined low-rank and full-rank approach for adaptation and personalization.