A Novel Federated Learning with Bidirectional Adaptive Differential Privacy

With the explosive growth of personal data in the era of big data,federated learning has broader application prospects,in order to solve the problem of data island and preserve user data privacy,a federated learning model based on differential privacy(DP)is proposed.Participants share the parameters after adding noise to the central server for parameter aggregation by training local data.However,there are two problems in this model:on the one hand,the data information in the process of broadcasting parameters by the central server is still compromised,with the risk of user privacy leakage;on the other hand,adding too much noise to parameters will reduce the quality of parameter aggregation and affect the model accuracy of federated learning finally.Therefore,a novel federated learning approach with bidirectional adaptive differential privacy(FedBADP)is proposed,it can adaptively add noise to the gradients transmitted by participants and central server,and protects data security without affecting model accuracy.In addition,considering the performance limitations of the participants’hardware devices,this model samples their gradients to reduce communication overhead,and uses RMSprop to accelerate the convergence of the model on the participants and central server to improve the ac-curacy of the model.Experiments show that our novel model can not only obtain better results in accuracy,but also enhance user privacy preserving while reducing communication overhead.

Adaptive Bidirectional Droop Control for Electric Vehicles Parking with Vehicle-to-grid Service in Microgrid

With the popularity of electric vehicles(EVs),a large number of EVs will become a burden to the future grid with arbitrary charging management.It is of vital significance to the control of the EVs charging and discharging state appropriately to enable the EVs to become friendly to the grid.Therefore,considering the potential for EVs seen as energy storage devices,this paper proposes a multiport DC-DC solid state transformer topology for bidirectional photovoltaic/battery-assisted EV parking lot with vehicle-to-grid service(V2G-PVBP).Relying on the energy storage function of EVs,V2G-PVBP is able to not only satisfy the normal requirements of EVs’owner,but also provide the function of load shifting and load regulation to the microgrid.In this paper,EVs are categorized into limited EV and freedom EV.Limited EVs are always kept in charging state and freedom EVs can take part in the load regulation of the microgrid.The proposed adaptive bidirectional droop control is designed for freedom EVs to make them autonomously charge or discharge with certain power which according to each EV’s state of charge,battery capacity,leaving time,and other factors to maintain the stability of the future microgrid.Eventually,the simulation and experiment of the adaptive bidirectional droop control based V2G-PVBP are provided to prove the availability of V2G-PVBP.