FedCLCC:A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing

Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.

Reduced-order modelling of urban wind environment and gaseous pollutants dispersion in an urban-scale street canyon

Hazardous chemical gases will spread rapidly after leakage.For emergency response to that,people need to obtain the information of wind and pollutant in time.However,it takes a lot of time to calculate the flow in large-scale urban areas by numerical simulation.Therefore,reduced-order model(ROM)is developed to improve the efficiency.In this paper,we propose a model based on proper orthogonal decomposition(POD)and radial basis function(RBF)interpolation.We validate the model by calculating the wind field and the pollutant propagation process in a 144 square kilometer area of Beijing.The results show that ROM can reduce CPU times by more than 99%at the cost of only 0.1%information loss,comparing with the traditional approach of computational fluid dynamics(CFD).