In recent years, SDN develops rapidly all around the world, known as a great renovation for the traditional network architecture. SDN principle advantages contain separation of hardware and software, separation of control and management, centralized management, open source, bringing low cost, high efficiency and flexible business. Power Grid has complicated application system, and data center is the most important, so introducing SDN can build a better IT infrastructure to supply application services. In order to drive network through application and enhance the convenience of application deployment and maintenance in datacenter, this paper presents a SDN group policy model with application centric. Firstly, a universal SDN based environment is developed, and the server physical location, security strategy and network address IP are decoupled by introducing and extending VXLAN technology. Secondly, combined with the grid application, a group policy control method is described in detail. The introduced SDN group policy control model will enhance the SDN centralized management and control functions, and simplify the network application deployment and maintenance.

Power load forecasting (PLF) has a positive impact on the stability of power systems and can reduce the cost of power generation enterprises. To improve the forecasting accuracy, more information besides load data is necessary. In recent years, a novel privacy-preserving paradigm vertical federated learning (FL) has been applied to PLF to improve forecasting accuracy while keeping different organizations’ data locally. However, two problems are still not well solved in vertical FL. The first problem is a lack of a full data-processing procedure, and the second is a lack of enhanced privacy protection for data processing. To address it, according to the procedure in a practical scenario, we propose a vertical FL XGBoost-based PLF, where multiparty secure computation is used to enhance the privacy protection of FL. Concretely, we design a full data-processing PLF, including data cleaning, private set intersection, feature selection, federated XGBoost training, and inference. Furthermore, we further use RSA encryption in the private set intersection and Paillier homomorphic encryption in the training and inference phases. To validate the proposed method, we conducted experiments to compare centralized learning and vertical FL on several real-world datasets. The proposed method can also be directly applied to other practical vertical FL tasks.

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With the construction of the modern power system, power load forecasting is significant to keep the electric Internet of Things in operation. However, it usually needs to collect massive power load data on the server and may face the problem of privacy leakage of raw data. Federated learning can enhance the privacy of the raw power load data of clients by frequently transmitting model updates. Concerning the increasing communication burden of resource-heterogeneous clients resulting from frequent communication with the server, a communication-efficient federated learning algorithm based on Compressed Model Updates and Lazy uploAd (CMULA-FL) was proposed to reduce the communication cost. CMULA-FL also integrates the error compensation strategy to improve the model utility. First, the compression operator is used to compress the transmitted model updates, of which large norms are uploaded to reduce the communication cost of each epoch and transmission frequency. Second, by measuring the error of compression and lazy upload, the error is accumulated to the next epoch to improve the model utility. Finally, based on simulation experiments on the benchmark power load data, the results show that the communication cost decreases at least 60% with controlled loss of model prediction compared with baseline. INDEX TERMS Power load forecasting, federated learning, quantization, lazy upload, error compensation.

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