Revenue generation for truthful spectrum auction in dynamic spectrum access

Spectrum is a critical yet scarce resource and it has been shown that dynamic spectrum access can significantly improve spectrum utilization. To achieve this, it is important to incentivize the primary license holders to open up their under-utilized spectrum for sharing. In this paper we present a secondary spectrum market where a primary license holder can sell access to its unused or under-used spectrum resources in the form of certain fine-grained spectrumspace-time unit. Secondary wireless service providers can purchase such contracts to deploy new service, enhance their existing service, or deploy ad hoc service to meet flash crowds demand. Within the context of this market, we investigate how to use auction mechanisms to allocate and price spectrum resources so that the primary license holder’s revenue is maximized. We begin by classifying a number of alternative auction formats in terms of spectrum demand. We then study a specific auction format where secondary wireless service providers have demands for fixed locations (cells). We propose an optimal auction based on the concept of virtual valuation. Assuming the knowledge of valuation distributions, the optimal auction uses the Vickrey-Clarke-Groves (VCG) mechanism to maximize the expected revenue while enforcing truthfulness. To reduce the computational complexity, we further design a truthful suboptimal auction with polynomial time complexity. It uses a monotone allocation and critical value payment to enforce truthfulness. Simulation results show that this suboptimal auction can generate stable expected revenue

Federated Learning with Reduced Information Leakage and Computation

Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data. Although local data is not exposed directly, privacy concerns nonetheless exist as clients' sensitive information can be inferred from intermediate computations. Moreover, such information leakage accumulates substantially over time as the same data is repeatedly used during the iterative learning process. As a result, it can be particularly difficult to balance the privacy-accuracy trade-off when designing privacy-preserving FL algorithms. In this paper, we introduce Upcycled-FL, a novel federated learning framework with first-order approximation applied at every even iteration. Under this framework, half of the FL updates incur no information leakage and require much less computation. We first conduct the theoretical analysis on the convergence (rate) of Upcycled-FL, and then apply perturbation mechanisms to preserve privacy. Experiments on real-world data show that Upcycled-FL consistently outperforms existing methods over heterogeneous data, and significantly improves privacy-accuracy trade-off while reducing 48% of the training time on average

Effects of sitagliptin on blood glucose, lipids, pancreatic β cell secretory function and insulin resistance in patients with type 2 diabetes mellitus

Purpose: To study the influence of sitagliptin on blood glucose, lipid and pancreatic β cell function in type II diabetes mellitus (T2DM) patients.Methods: Two groups of T2DM patients (100/group) received either metformin only (control group), or metformin plus sitaglipin (study group) for 3 months. Blood lipid profiles, fasting blood glucose (FBG), indices of pancreatic function and insulin resistance were assayed using standard biochemical methods.Results: The metformin-sitaglipin combination resulted in significant decreases in FBG, 2-h PBG, HbAlc total cholesterol (TC), triacylglycerol, and low-density lipoprotein cholesterol (LDL-C), when compared with the metformin-only treatment (p < 0.05). Although there were significant decreases in pancreatic secretion of insulin, fasting insulin, and 2 h postprandial insulin in the two groups, these parameters were significantly lower in the metformin-only treated patients than in those with combination treatment (p < 0.05).Conclusion: Sitagliptin normalizes fasting blood glucose, lipid profiles and insulin secretion in type II diabetes mellitus (T2DM) patients.Keywords: Type 2 diabetes mellitus, Sitagliptin, Blood glucose, Pancreatic islet function, Insuli