Federated learning (FL) is a distributed machine learning framework that can effectively help multiple players to use data to train federated models while complying with their privacy, data security, and government regulations. Due to federated model training, an accurate model should be trained, and all federated players should actively participate. Therefore, it is crucial to design an incentive mechanism; however, there is a conflict between fairness and Pareto efficiency in the incentive mechanism. In this paper, we propose an incentive mechanism via the combination of the Shapley value and Pareto efficiency optimization, in which a third party is introduced to supervise the federated payoff allocation. If the payoff can reach Pareto optimality, the federated payoff is allocated by the Shapley value method; otherwise, the relevant federated players are punished. Numerical and simulation experiments show that the mechanism can achieve fair payoff allocation and Pareto optimality payoff allocation. The Nash equilibrium of this mechanism is formed when Pareto optimality payoff allocation is achieved.

A 128-b rapid single-flux-quantum (RSFQ) Advanced Encryption Standard (AES) encryption accelerator based on bit-slice architecture is proposed for the first time. Unlike the traditional methods, the method of looking up only one 256-B table to complete the entire AES round function is proposed. The proposed method can reduce nearly 50% of the hardware cost compared to the traditional method. The simple lookup table, shift, and xor operations are used in the proposed accelerator where one 256-B table needs to be stored for lookup table operations. The RSFQ logic circuits of the proposed accelerator are designed and simulated at the logic level. It consists of 136 558 JJs based on the Open Dataset of CONNECT Cell Library for AIST ADP2. The simulation results show the proposed accelerator works correctly.

Service pricing is a bottleneck in the development of innovation services, as it is the issue of most concern to the suppliers and demanders. In this paper, a negotiated pricing model that is based on the multiobjective genetic algorithm is developed for innovation services. Regarding the process of service pricing as a multiobjective problem, the objective functions which include the service price, service efficiency, and service quality for the suppliers and the demanders are constructed. Because the solution of a multiobjective problem is typically a series of alternatives, an additional negotiation process is necessary in determining the final decision. A learning strategy is adopted during the negotiation process to simulate reality. Finally, the model is implemented in an innovation service transaction, the objective of which is to select the optimal price plan. The results demonstrate that the model is able to provide quantitative decision support for the pricing of an innovation service with a win-win result obtained. Furthermore, the influence of the parameters during the negotiation process is analyzed in detail. The effects of the learning strategy in accelerating the negotiation process and the set of reasonable parameters are discussed.