Federated learning(FL), enabling multiple clients collaboratively to train a model via a parameter server, is an effective approach to address the issue of data silos. However, due to the self-interest and laziness of servers, they may not correctly aggregate the global model parameters, which will cause the final model trained to deviate from the training goal. In the existing proposals, the cryptography-based verification scheme involves heavy computation overheads. On the other hand, the replication-based verification method, relying on a dual-server architecture, can ensure the correctness of aggregation and reduce computation overheads, but incur at least twice the communication cost as that of the task itself. To address these issues, we propose a novel replication-based aggregation scheme for FL, which enables efficient verification and stronger correctness assurance. The scheme employs a main-secondary server architecture, which allows the secondary servers to partakes in aggregation tasks at a predetermined probability, consequently mitigating the validation overhead. Moreover, we resort to the game theory and design a Learning Contract to impose penalties on dishonest servers, enforcing rational servers to correctly compute global model parameters. Under the use of Betrayal Contract to prevent collusion among servers, we further design a training game to efficiently verify global model parameters and ensure their correctness. Finally, we analyze the correctness of the proposed scheme and demonstrate that the computational overhead of our scheme is$\frac{{n + 1}}{{2n}}$of the previous replication-based validation scheme, obtaining a significant reduction in communication cost, where$n$means the training rounds. Experimental results further validate our deduction.

Abstract The energy system is evolving from smart grid to energy Internet. Virtual power plant (VPP), as an important part of the energy Internet, plays an important role in the distributed energy generation and trading. In this article, a blockchain‐based VPP transaction model is established for the future energy Internet driven by real‐time electricity price. Then the smart contracts for distributed energy trading in VPPs using blockchain technology are proposed, and the key technological difficulties are analyzed and the solutions are given. Experiments show that the proposed model can reflect the supply and demand information in real time, so that two‐way selection can be carried out under the condition of information symmetry when distributed energy is connected to the grid. If our method is applied, we can help distributed energy suppliers set electricity prices, reduce the trust cost and improve the energy trading efficiency. Also, we can help the distributed energy voluntarily participate in VPPs and joint maintain the system, then solve the problem of VPP's coordinated control and scheduling of distributed energy resources.

Abstract Both of an automatic classification method for original documents based on image feature and a layout analysis method based on rule hypothesis tree are proposed. Then an intelligent document‐filling system by electronizing the original documents, which can be applied to cellphones and pads is designed. When users are filling documents online, information can be automatically input to the financial information system merely by taking photos of the original documents. By this means can not only save time but also ensure the accuracy between the data online and that on the original documents. Experiments show that the accuracy of document classification is 88.38%, the accuracy of document‐filling is 87.22%, and it takes 5.042 seconds dealing with per document. The system can be applied to financial, government, libraries, electric power, enterprises and many other industries, which has high economic and application value.