Art Market Issue 1,2024 Focus on the 2023 Domestic ArtFair&AuctionMarket The domestic art market in 2023 has recoveredfromthe pandemic,with galleries,art fairs,and brick-and-mortar markets in the primary market re...Art Market Issue 1,2024 Focus on the 2023 Domestic ArtFair&AuctionMarket The domestic art market in 2023 has recoveredfromthe pandemic,with galleries,art fairs,and brick-and-mortar markets in the primary market resuming offline operations and experiencing rapid growth.The auction industry for cultural artifacts and artworks in the secondary market is also thriving,with bustling on-site previews and bidding.展开更多
The incentive mechanism of federated learning has been a hot topic,but little research has been done on the compensation of privacy loss.To this end,this study uses the Local SGD federal learning framework and gives a...The incentive mechanism of federated learning has been a hot topic,but little research has been done on the compensation of privacy loss.To this end,this study uses the Local SGD federal learning framework and gives a theoretical analysis under the use of differential privacy protection.Based on the analysis,a multi‐attribute reverse auction model is proposed to be used for user selection as well as payment calculation for participation in federal learning.The model uses a mixture of economic and non‐economic attributes in making choices for users and is transformed into an optimisation equation to solve the user choice problem.In addition,a post‐auction negotiation model that uses the Rubinstein bargaining model as well as optimisation equations to describe the negotiation process and theoretically demonstrate the improvement of social welfare is proposed.In the experimental part,the authors find that their algorithm improves both the model accuracy and the F1‐score values relative to the comparison algorithms to varying degrees.展开更多
文摘Art Market Issue 1,2024 Focus on the 2023 Domestic ArtFair&AuctionMarket The domestic art market in 2023 has recoveredfromthe pandemic,with galleries,art fairs,and brick-and-mortar markets in the primary market resuming offline operations and experiencing rapid growth.The auction industry for cultural artifacts and artworks in the secondary market is also thriving,with bustling on-site previews and bidding.
基金National Natural Science Foundation of China,Grant Number:62062020National Natural Science Foundation of China,Grant Number:72161005Technology Foundation of Guizhou Province,Grant Number:QianKeHeJiChu‐ZK[2022]‐General184.
文摘The incentive mechanism of federated learning has been a hot topic,but little research has been done on the compensation of privacy loss.To this end,this study uses the Local SGD federal learning framework and gives a theoretical analysis under the use of differential privacy protection.Based on the analysis,a multi‐attribute reverse auction model is proposed to be used for user selection as well as payment calculation for participation in federal learning.The model uses a mixture of economic and non‐economic attributes in making choices for users and is transformed into an optimisation equation to solve the user choice problem.In addition,a post‐auction negotiation model that uses the Rubinstein bargaining model as well as optimisation equations to describe the negotiation process and theoretically demonstrate the improvement of social welfare is proposed.In the experimental part,the authors find that their algorithm improves both the model accuracy and the F1‐score values relative to the comparison algorithms to varying degrees.
