Federated learning papers tackle label scarcity, adversarial attacks, and modality gaps

New arXiv papers propose methods for federated learning: FedLNS mitigates adversarial manipulation in federated LLMs, FedCoRe handles missing modalities in healthcare, and FedImp improves convergence under non-IID data. A unified framework for federated prompt learning is also presented.