As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improveme...As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.展开更多
This paper optimizes the meteorological drought assessment method in Fujian Province, China, making it more suitable for long-term serial drought statistics. Using the precipitation data of Pingtan National Basic Weat...This paper optimizes the meteorological drought assessment method in Fujian Province, China, making it more suitable for long-term serial drought statistics. Using the precipitation data of Pingtan National Basic Weather Station from 1971 to 2016, according to the optimized meteorological drought assessment method, statistics Pingtan meteorological drought process, and its characteristics analysis;through the multi-model integrated forecasting super-aggregate method, the 2011-2014 Fujian precipitation reality and the European Center, T639, WRF, Japan and other numerical forecast products were used for comparative analysis. The use of multiple regression method was used to forecast precipitation, by the localized precipitation correction scheme. The early warning method of single station meteorological drought process based on precipitation reality and precipitation forecast is obtained, and the platformization idea is proposed.展开更多
基金National Natural Science Foundation of China,Grant/Award Number:62272114Joint Research Fund of Guangzhou and University,Grant/Award Number:202201020380+3 种基金Guangdong Higher Education Innovation Group,Grant/Award Number:2020KCXTD007Pearl River Scholars Funding Program of Guangdong Universities(2019)National Key R&D Program of China,Grant/Award Number:2022ZD0119602Major Key Project of PCL,Grant/Award Number:PCL2022A03。
文摘As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.
文摘This paper optimizes the meteorological drought assessment method in Fujian Province, China, making it more suitable for long-term serial drought statistics. Using the precipitation data of Pingtan National Basic Weather Station from 1971 to 2016, according to the optimized meteorological drought assessment method, statistics Pingtan meteorological drought process, and its characteristics analysis;through the multi-model integrated forecasting super-aggregate method, the 2011-2014 Fujian precipitation reality and the European Center, T639, WRF, Japan and other numerical forecast products were used for comparative analysis. The use of multiple regression method was used to forecast precipitation, by the localized precipitation correction scheme. The early warning method of single station meteorological drought process based on precipitation reality and precipitation forecast is obtained, and the platformization idea is proposed.
