分布式人工智能（Distributed Artificial Intelligence）是指利用多个智能体（即个体的软件或硬件实体）并且这些智能体可以独立地工作和交互的人工智能系统。分布式人工智能研究领域关注如何利用多个智能体之间的分布式协作和交互来解决复杂的问题，涉及到问题求解、学习与决策、智能体交互与协作、系统管理与安全、性能优化与扩展性等方面的内容。这一领域的研究旨在推动人工智能系统向着更加分布式、协作和智能化的方向发展。
Distributed Artificial Intelligence refers to an artificial intelligence system that utilizes multiple agents (i.e., individual software or hardware entities) and these agents can work and interact independently. The field of distributed artificial intelligence research focuses on how to use distributed collaboration and interaction between multiple agents to solve complex problems, involving problem solving, learning and decision-making, agent interaction and collaboration, system management and security, performance optimization and Scalability and other aspects. Research in this field aims to promote the development of artificial intelligence systems in a more distributed, collaborative and intelligent direction.

联邦学习（Federated Learning）是一种机器学习方法，旨在通过在分散的设备或服务器上进行模型训练，而无需将原始数据集集中到单个中心位置。这种分散式的学习方式具有隐私保护、数据安全和降低通信成本的优势。联邦学习研究领域的主要内容涵盖了隐私保护、通信效率、模型聚合、非独立同分布数据和安全性等方面，旨在解决联邦学习中的各种挑战和问题，推动其在实际应用中的发展和应用。
Federated Learning is a machine learning method designed to train models on distributed devices or servers without centralizing the original data set to a single central location. This decentralized learning method has the advantages of privacy protection, data security and reduced communication costs. The main contents of the federated learning research field cover privacy protection, communication efficiency, model aggregation, non-independent and identically distributed data and security, etc., aiming to solve various challenges and problems in federated learning and promote its development in practical applications. and applications.

医学影像分析是指利用计算机视觉和机器学习技术对医学影像数据进行处理和分析，以帮助医生进行疾病诊断、治疗规划和预后评估等工作。医学影像分析研究领域的主要内容包括图像分割、病变检测与识别、图像配准、特征提取与选择、深度学习在医学影像中的应用以及辅助诊断与预后评估等方面，旨在提高医学影像分析的自动化程度和准确性，为临床诊断和治疗提供更有效的辅助手段。
Medical image analysis refers to the use of computer vision and machine learning technology to process and analyze medical imaging data to help doctors perform disease diagnosis, treatment planning, and prognosis assessment. The main contents of the medical image analysis research field include image segmentation, lesion detection and identification, image registration, feature extraction and selection, the application of deep learning in medical images, and auxiliary diagnosis and prognosis assessment, etc., aiming to improve the efficiency of medical image analysis. The degree of automation and accuracy provide more effective auxiliary means for clinical diagnosis and treatment.

人工智能应用研究领域致力于将机器学习、深度学习等人工智能技术应用于智慧交通、智慧医疗等领域，以解决现实世界中的各种问题，提高生活质量和社会效率。在智慧交通应用领域中，主要使用时序数据预测模型和时空图模型进行交通流量预测、时空网络预测、智能导航系统。在智慧医疗应用领域，主要利用计算机视觉和计算生物学算法，对海量医疗数据进行计算和智能化分析。
The field of artificial intelligence application research is committed to applying artificial intelligence technologies such as machine learning and deep learning to smart transportation, smart medical care and other fields to solve various problems in the real world and improve the quality of life and social efficiency. In the field of smart transportation applications, time series data prediction models and spatiotemporal graph models are mainly used for traffic flow prediction, spatiotemporal network prediction, and intelligent navigation systems. In the field of smart medical applications, computer vision and computational biology algorithms are mainly used to calculate and intelligently analyze massive medical data.