The field of artificial intelligence is moving towards personalized and efficient models, with a focus on adapting large language models to meet specific user needs while maintaining privacy and computational efficiency. Researchers are exploring the potential of federated learning and collaborative mechanisms between large and small language models to achieve this goal. The integration of large language models with federated learning is also being investigated, with a focus on addressing challenges such as communication and computation overheads, heterogeneity, and privacy and security concerns. Noteworthy papers include:

• Towards Artificial General or Personalized Intelligence, which proposes personalized federated intelligence as a key enabling technique for deploying foundation models at the edge with improved personalization, computational efficiency, and privacy guarantees.
• A Survey on Collaborative Mechanisms Between Large and Small Language Models, which provides a comprehensive overview of interaction mechanisms and enabling technologies for collaboration between large and small language models.