虚拟电厂高效运行依赖海量异构数据交互，面临数据价值挖掘与隐私保护的根本矛盾。针对中心化管理的数据孤岛、单点故障及隐私泄露问题，本文综述了联邦学习与可信执行环境在虚拟电厂的应用现状与融合方案。通过对比分析区块链、加密协作等技术方案，阐明了联邦学习在隐私保护、协作效率和技术适配性方面的优势。系统梳理了联邦学习在虚拟电厂负荷预测、分布式能源资源协同、电力市场交易及需求响应中的应用，并分析了可信执行环境基于硬件隔离保障计算机密性与完整性的机制。重点剖析了两者的融合架构：可信执行环境通过保护模型聚合过程和敏感数据处理弥补联邦学习在参数泄露与投毒攻击防护上的不足，联邦学习的分布式特性突破可信硬件的性能与扩展性瓶颈。该融合技术实现了"数据可用不可见"，为构建安全高效的能源互联网数据协作提供可行路径，并对性能优化、侧信道防御及标准化等挑战进行了展望。

The efficient operation of Virtual Power Plants (VPPs) relies on massive heterogeneous data interactions, facing the fundamental contradiction between data value mining and privacy protection. Addressing the challenges of data silos, single points of failure, and privacy leakage in centralized management, this paper reviews the application status and integration solutions of federated learning and trusted execution environment in VPPs. Through comparative analysis of blockchain and cryptographic collaboration schemes, the advantages of federated learning in privacy protection, collaboration efficiency, and technical adaptability are clarified. The applications of federated learning in VPP load forecasting, distributed energy resource coordination, electricity market trading, and demand response are systematically reviewed, along with an analysis of trusted execution environment mechanisms that ensure computational confidentiality and integrity through hardware isolation. The integration architecture of both technologies is emphasized: trusted execution environment addresses federated learning's vulnerabilities in parameter leakage and poisoning attack defense by protecting model aggregation processes and sensitive data processing, while federated learning's distributed characteristics overcome the performance and scalability bottlenecks of trusted hardware. This integrated technology realizes "data availability without visibility," providing a feasible path for building secure and efficient energy internet data collaboration, with prospects for challenges in performance optimization, side-channel defense, and standardization.