2,312 publications from this institution
Asymptotic behavior of all solutions of the delay two-dimensional logistic system x/sub m+1,/ /sub n/+ax/sub m,/ /sub n+1/=/spl mu//sub mn/x/sub mn/ (1-x/sub m-/spl sigma/, n-/spl tau//) is investigated. Some sufficient conditions for the stability of this equation are derived. Moreover, some sufficient and necessary conditions for oscillations of all solutions of this equation are obtained.
As a representative emerging machine learning technique, federated learning (FL) has gained considerable popularity for its special feature of "making data available but not visible". However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable distribution. These shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant (CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to FL. Finally, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.
