Optimal Asset Allocation Subject to Withdrawal Risk and Solvency Constraints

This paper investigates the optimal asset allocation of a financial institution whose customers are free to withdraw their capital-guaranteed financial contracts at any time. In accounting for the asset-liability mismatch risk of the institution, we present a general utility optimization problem in a discrete-time setting and provide a dynamic programming principle for the optimal investment strategies. Furthermore, we consider an explicit context, including liquidity risk, interest rate, and credit intensity fluctuations, and show by numerical results that the optimal strategy improves both the solvency and asset returns of the institution compared to a standard institutional investor’s asset allocation.

Utility Optimization of Federated Learning with Differential Privacy

Secure and trusted cross-platform knowledge sharing is significant for modern intelligent data analysis. To address the trade-off problems between privacy and utility in complex federated learning, a novel differentially private federated learning framework is proposed. First, the impact of data heterogeneity of participants on global model accuracy is analyzed quantitatively based on 1-Wasserstein distance. Then, we design a multilevel and multiparticipant dynamic allocation method of privacy budget to reduce the injected noise, and the utility can be improved efficiently. Finally, they are integrated, and a novel adaptive differentially private federated learning algorithm (A-DPFL) is designed. Comprehensive experiments on redefined non-I.I.D MNIST and CIFAR-10 datasets are conducted, and the results demonstrate the superiority of model accuracy, convergence, and robustness.

An Overview of Current Multi-Attribute Techniques in Double Auction Frameworks

Double auction is becoming the preferred negotiation protocol for cloud service negotiation due to its economic efficiency, capability in facilitating dynamic pricing, and suitability for handling a large number of customers and service providers. However, as far as this research work is concerned, there is no framework using double auction that simultaneously and comprehensively addresses the heterogeneity of cloud services in multi-attributes negotiation. Before such a framework can be designed, suitable multi-attributes negotiation techniques and its attributes should be identified. Therefore, this paper’s objective is to distinguish and provide an overview of multi-attributes techniques used in double auction negotiations. The sources are from the Scopus Database. It is found that the current multi-attributes techniques lacked in addressing the preferential dependency, selective attributes and utility optimization simultaneously in double auction frameworks. These concerns need to be addressed in order to devise a practical framework. The future direction for double auction framework in multi-attributes negotiation is suggested.