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.