Logit Poisoning Attack in Distillation-based Federated Learning and its Countermeasures

Distillation-based federated learning has emerged as a promising collaborative learning approach, where clients share the output logit vectors of a public dataset rather than their private model parameters. This practice reduces the risk of privacy invasion attacks and facilitates heterogeneous learning. The landscape of poisoning attacks within distillation-based federated learning is complex, with existing research employing traditional data poisoning strategies targeting the models' parameters. However, these attack schemes primarily have shortcomings rooted in their original designs, which target the model parameters rather than the logit vectors. Furthermore, they do not adequately consider the role of logit vectors in carrying information during the knowledge transfer process. This misalignment results in less efficiency in the context of distillation-based federated learning. Due to the limitations of existing methodologies, our research delves into the intrinsic properties of the logit vector, striving for a more nuanced understanding. We introduce a two-stage scheme for logit poisoning attacks, addressing previous shortcomings. Initially, we collect the local logits, generate the representative vectors, categorize the logit elements within the vector, and design a shuffling table to maximize information entropy. Then, we intentionally scale the shuffled logit vectors to enhance the magnitude of the target vectors. Concurrently, we propose an efficient defense algorithm to counter this new poisoning scheme by calculating the distance between estimated benign vectors and vectors uploaded by users. Through extensive experiments, our study illustrates the significant threat posed by the proposed logit poisoning attack and highlights the effectiveness of our defense algorithm

A Quasi-ARX Model for Multivariable Decoupling Control of Nonlinear MIMO System

This paper proposes a multiinput and multioutput (MIMO) quasi-autoregressive eXogenous (ARX) model and a multivariable-decoupling proportional integral differential (PID) controller for MIMO nonlinear systems based on the proposed model. The proposed MIMO quasi-ARX model improves the performance of ordinary quasi-ARX model. The proposed controller consists of a traditional PID controller with a decoupling compensator and a feed-forward compensator for the nonlinear dynamics based on the MIMO quasi-ARX model. Then an adaptive control algorithm is presented using the MIMO quasi-ARX radial basis function network (RBFN) prediction model and some stability analysis of control system is shown. Simulation results show the effectiveness of the proposed control method

How does internal and external CSR affect employees’ work engagement? Exploring multiple mediation mechanisms and boundary conditions

We investigate the different mechanisms concerning how employees’ perceptions of external and internal corporate social responsibility (CSR) serve to influence employees’ work engagement. By combining social exchange theory and social identity theory, we implement and examine an integrated moderated mediation framework in which employees’ value orientations (e.g., collectivism or individualism) impact the mediating mechanism between their perceived external and internal CSR, organizational pride and perceived organizational support (POS), and work engagement. This work fills a research gap to examine the indirect relationship between employees’ perceptions of external and internal CSR and work engagement. Using two periods of survey data from 250 working employees in China, we find that employees’ perceptions of external CSR positively influence work engagement via organizational pride. The value of collectivism strengthens the direct effect of employees’ perceptions of external CSR on work engagement, and the indirect effect of employees’ perceptions of external CSR on work engagement via organizational pride. Moreover, employees’ perceptions of internal CSR positively influence work engagement via POS. The value of individualism strengthens the direct effect of employees’ perceptions of internal CSR on work engagement, and the indirect effect of employees’ perceptions of internal CSR on work engagement via POS. The results contribute to both theory and practice

Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method