A Bargaining-based Approach for Feature Trading in Vertical Federated Learning

Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training across the data and the task parties with different features about the same user set while preserving data privacy. In production environment, VFL usually involves one task party and one data party. Fair and economically efficient feature trading is crucial to the commercialization of VFL, where the task party is considered as the data consumer who buys the data party's features. However, current VFL feature trading practices often price the data party's data as a whole and assume transactions occur prior to the performing VFL. Neglecting the performance gains resulting from traded features may lead to underpayment and overpayment issues. In this study, we propose a bargaining-based feature trading approach in VFL to encourage economically efficient transactions. Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties. We analyze the proposed bargaining model under perfect and imperfect performance information settings, proving the existence of an equilibrium that optimizes the parties' objectives. Moreover, we develop performance gain estimation-based bargaining strategies for imperfect performance information scenarios and discuss potential security issues and solutions. Experiments on three real-world datasets demonstrate the effectiveness of the proposed bargaining model

Cell-free fat extract regulates oxidative stress and alleviates Th2-mediated inflammation in atopic dermatitis

Atopic dermatitis (AD) is a common inflammatory skin disease that significantly affects patients’ quality of life. This study aimed to evaluate the therapeutic potential of cell-free fat extract (FE) in AD. In this study, the therapeutic effect of DNCB-induced AD mouse models was investigated. Dermatitis scores and transepidermal water loss (TEWL) were recorded to evaluate the severity of dermatitis. Histological analysis and cytokines measurement were conducted to assess the therapeutic effect. Additionally, the ability of FE to protect cells from ROS-induced damage and its ROS scavenging capacity both in vitro and in vivo were investigated. Furthermore, we performed Th1/2 cell differentiation with and without FE to elucidate the underlying therapeutic mechanism. FE reduced apoptosis and cell death of HaCat cells exposed to oxidative stress. Moreover, FE exhibited concentration-dependent antioxidant activity and scavenged ROS both in vitro and vivo. Treatment with FE alleviated AD symptoms in mice, as evidenced by improved TEWL, restored epidermis thickness, reduced mast cell infiltration, decreased DNA oxidative damage and lower inflammatory cytokines like IFN-γ, IL-4, and IL-13. FE also inhibited the differentiation of Th2 cells in vitro. Our findings indicate that FE regulates oxidative stress and mitigates Th2-mediated inflammation in atopic dermatitis by inhibiting Th2 cell differentiation, suggesting that FE has the potential as a future treatment option for AD

MM Algorithms for Variance Components Models

Variance components estimation and mixed model analysis are central themes in statistics with applications in numerous scientific disciplines. Despite the best efforts of generations of statisticians and numerical analysts, maximum likelihood estimation and restricted maximum likelihood estimation of variance component models remain numerically challenging. Building on the minorization-maximization (MM) principle, this paper presents a novel iterative algorithm for variance components estimation. Our MM algorithm is trivial to implement and competitive on large data problems. The algorithm readily extends to more complicated problems such as linear mixed models, multivariate response models possibly with missing data, maximum a posteriori estimation, and penalized estimation. We establish the global convergence of the MM algorithm to a Karush-Kuhn-Tucker (KKT) point and demonstrate, both numerically and theoretically, that it converges faster than the classical EM algorithm when the number of variance components is greater than two and all covariance matrices are positive definite

MM ALGORITHMS FOR VARIANCE COMPONENT ESTIMATION AND SELECTION IN LOGISTIC LINEAR MIXED MODEL.

Logistic linear mixed models are widely used in experimental designs and genetic analyses of binary traits. Motivated by modern applications, we consider the case of many groups of random effects, where each group corresponds to a variance component. When the number of variance components is large, fitting a logistic linear mixed model is challenging. Thus, we develop two efficient and stable minorization-maximization (MM) algorithms for estimating variance components based on a Laplace approximation of the logistic model. One of these leads to a simple iterative soft-thresholding algorithm for variance component selection using the maximum penalized approximated likelihood. We demonstrate the variance component estimation and selection performance of our algorithms by means of simulation studies and an analysis of real data

MM ALGORITHMS FOR VARIANCE COMPONENT ESTIMATION AND SELECTION IN LOGISTIC LINEAR MIXED MODEL

Logistic linear mixed models are widely used in experimental designs and genetic analyses of binary traits. Motivated by modern applications, we consider the case of many groups of random effects, where each group corresponds to a variance component. When the number of variance components is large, fitting a logistic linear mixed model is challenging. Thus, we develop two efficient and stable minorization-maximization (MM) algorithms for estimating variance components based on a Laplace approximation of the logistic model. One of these leads to a simple iterative soft-thresholding algorithm for variance component selection using the maximum penalized approximated likelihood. We demonstrate the variance component estimation and selection performance of our algorithms by means of simulation studies and an analysis of real data

Mitochondria-targeted ubiquinone (MitoQ) enhances acetaldehyde clearance by reversing alcohol-induced posttranslational modification of aldehyde dehydrogenase 2: A molecular mechanism of protection against alcoholic liver disease

Alcohol metabolism in the liver generates highly toxic acetaldehyde. Breakdown of acetaldehyde by aldehyde dehydrogenase 2 (ALDH2) in the mitochondria consumes NAD+ and generates reactive oxygen/nitrogen species, which represents a fundamental mechanism in the pathogenesis of alcoholic liver disease (ALD). A mitochondria-targeted lipophilic ubiquinone (MitoQ) has been shown to confer greater protection against oxidative damage in the mitochondria compared to untargeted antioxidants. The present study aimed to investigate if MitoQ could preserve mitochondrial ALDH2 activity and speed up acetaldehyde clearance, thereby protects against ALD. Male C57BL/6 J mice were exposed to alcohol for 8 weeks with MitoQ supplementation (5 mg/kg/d) for the last 4 weeks. MitoQ ameliorated alcohol-induced oxidative/nitrosative stress and glutathione deficiency. It also reversed alcohol-reduced hepatic ALDH activity and accelerated acetaldehyde clearance through modulating ALDH2 cysteine S-nitrosylation, tyrosine nitration and 4-hydroxynonenol adducts formation. MitoQ ameliorated nitric oxide (NO) donor-mediated ADLH2 S-nitrosylation and nitration in Hepa-1c1c7 cells under glutathion depletion condition. In addition, alcohol-increased circulating acetaldehyde levels were accompanied by reduced intestinal ALDH activity and impaired intestinal barrier. In accordance, MitoQ reversed alcohol-increased plasma endotoxin levels and hepatic toll-like receptor 4 (TLR4)-NF-κB signaling along with subsequent inhibition of inflammatory cell infiltration. MitoQ also reversed alcohol-induced hepatic lipid accumulation through enhancing fatty acid β-oxidation. Alcohol-induced ER stress and apoptotic cell death signaling were reversed by MitoQ. This study demonstrated that speeding up acetaldehyde clearance by preserving ALDH2 activity critically mediates the beneficial effect of MitoQ on alcohol-induced pathogenesis at the gut-liver axis. Keywords: Aldehyde dehydrogenase 2, Posttranslational modification, Alcoholic liver disease, Mito

New understandings of the lithofacies paleogeography of the middle assemblage of Majiagou Fm in the Ordos Basin and its exploration significance

Accurate lithofacies-paleogeographic reconstruction is of great significance in predicting the dolomite reservoir distribution of the middle assemblage of Ordovician Majiagou Fm in the Ordos Basin. In this paper, the controlling effects of palaeotectonic background over sedimentation were first analyzed. Then the sedimentary mode of the middle assemblage was established and the lithofacies-paleogeography was reconstructed objectively for three intervals (Ma55, Ma57 and Ma59), based on the observation results of a large number of drilling cores and rock sections, together with the results of logging interpretation of rock composition and structure, single factor maps analysis and seismic data interpretation. The following findings were obtained. First, the middle assemblage of Majiagou Fm presents the uplift-depression alternation; two secondary low uplift zones extending in NS, i.e. Wushen Banner–Wuqi and Shenmu–Yulin–Yan'an, are developed in the eastern side of the central paleo-uplift, between which there is intraplatform depression, and lagoons are deposited in the Mizhi area in the east of the basin. Second, in the Ordos Basin, four NE-trending rift troughs are developed in the Proterozoic, which greatly affects the Ordovician sedimentary pattern and controls the distribution of intraplatform grain banks. Third, influenced jointly by the uplift-depression alternation and the intraplatform rift troughs of the Proterozoic, the intraplatform grain banks in the middle assemblage are mainly developed in the two low uplift zones, i.e. Shenmu–Yulin–Yan'an and Wushen Banner–Wuqi, trending NE in a similar echelon distribution. In conclusion, the two low uplift zones are the main development areas for high-quality carbonate reservoirs within the middle assemblage of Majiagou Fm in the basin

FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning

The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications. However, federated graph learning (FGL), even though graph data are prevalent, has not been well supported due to its unique characteristics and requirements. The lack of FGL-related framework increases the efforts for accomplishing reproducible research and deploying in real-world applications. Motivated by such strong demand, in this paper, we first discuss the challenges in creating an easy-to-use FGL package and accordingly present our implemented package FederatedScope-GNN (FS-G), which provides (1) a unified view for modularizing and expressing FGL algorithms; (2) comprehensive DataZoo and ModelZoo for out-of-the-box FGL capability; (3) an efficient model auto-tuning component; and (4) off-the-shelf privacy attack and defense abilities. We validate the effectiveness of FS-G by conducting extensive experiments, which simultaneously gains many valuable insights about FGL for the community. Moreover, we employ FS-G to serve the FGL application in real-world E-commerce scenarios, where the attained improvements indicate great potential business benefits. We publicly release FS-G, as submodules of FederatedScope, at https://github.com/alibaba/FederatedScope to promote FGL's research and enable broad applications that would otherwise be infeasible due to the lack of a dedicated package.Comment: Accpeted by KDD'2022; We have released FederatedScope for users on https://github.com/alibaba/FederatedScop