An Incentive Mechanism for Federated Learning:A Continuous Zero-Determinant Strategy Approach

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.

Improving Federated Learning through Abnormal Client Detection and Incentive

Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Incentive-compatible and budget balanced AGV mechanism for peer-to-peer energy trading in smart grids

Peer-to-peer(P2P)energy trading refers to a type of decentralized transaction,where the energy from distributed energy resources is directly traded between peers.A key challenge in peer-to-peer energy trading is designing a safe,efficient,and transparent trading model and operating mechanism.In this study,we consider a P2P trading environment based on blockchain technology,where prosumers can submit bids or offers without knowing the reports of others.We propose an Arrow-d’Aspremont-Gerard-Varet(AGV)-based mechanism to encourage prosumers to submit their real reserve price and determine the P2P transaction price.We demonstrate that the AGV mechanism can achieve Bayesian incentive compatibility and budget balance.Kernel density estimation(KDE)is used to derive the prior distribution from the historical bid/offer information of the agents.Case studies are carried out to analyze and evaluate the proposed mechanism.Simulation results verify the effectiveness of the proposed mechanism in guiding agents to report the true reserve price while maximizing social welfare.Moreover,we discuss the advantages of budget balance for decentralized trading by comparing the Vickrey-Clarke-Groves(VCG)and AGV mechanisms.

An Incentive Mechanism Model for Crowdsensing with Distributed Storage in Smart Cities

Crowdsensing,as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information,has received extensive attention in data collection.Since crowdsensing relies on user equipment to consume resources to obtain information,and the quality and distribution of user equipment are uneven,crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data,which affects the widespread use of crowdsensing.This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain,such as immutability and openness.An architecture for constructing a crowdsensing incentive mechanism under distributed incentives is proposed.A multi-attribute auction algorithm and a k-nearest neighbor-based sensing data quality determination algorithm are proposed to support the architecture.Participating users upload data,determine data quality according to the algorithm,update user reputation,and realize the selection of perceived data.The process of screening data and updating reputation value is realized by smart contracts,which ensures that the information cannot be tampered with,thereby encouraging more users to participate.Results of the simulation show that using two algorithms can well reflect data quality and screen out malicious data.With the help of blockchain performance,the architecture and algorithm can achieve decentralized storage and tamper-proof information,which helps to motivate more users to participate in perception tasks and improve data quality.

Do Research Incentives Promote Researchers’Mental Health?

Background:Researchers have a higher risk of anxiety and depression than the general population,so it is important to promote researchers’mental health.Method:Based on the data from 3210 global researchers surveyed by the journal Nature in 2021,confirmatory factor analysis,OLS regression and other regressions were used to explore the research incentive dimensions and their effects on researchers’mental health.Results:(1)Material incentive factors,work-family life balance factors,good organizational environment and spiritual motivation had significant positive effects on researchers’mental health.(2)The spiritual motivation could better promote researchers’mental health than the other factors.(3)Heterogeneity analysis showed that material incentive factors and spiritual motivation created more significant stimulating effects on the mental health of humanities and social sciences researchers.Work-family life balance factors were more effective in promoting the mental health of the mid-career group and the overtime group.Conclusion:Application of the four research incentives resulted in lower likelihood of anxiety or depression among researchers,and special attention should be paid to the role of the spiritual motivation.In order to promote researchers’mental health,different incentives should be applied to different researcher groups to better improve researchers’mental health.

A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G

As 5G becomes commercial,researchers have turned attention toward the Sixth-Generation(6G)network with the vision of connecting intelligence in a green energy-efficient manner.Federated learning triggers an upsurge of green intelligent services such as resources orchestration of communication infrastructures while preserving privacy and increasing communication efficiency.However,designing effective incentives in federated learning is challenging due to the dynamic available clients and the correlation between clients'contributions during the learning process.In this paper,we propose a dynamic incentive and reputation mechanism to improve energy efficiency and training performance of federated learning.The proposed incentive based on the Stackelberg game can timely adjust optimal energy consumption with changes in available clients during federated learning.Meanwhile,clients’contributions in reputation management are formulated based on the cooperative game to capture the correlation between tasks,which satisfies availability,fairness,and additivity.The simulation results show that the proposed scheme can significantly motivate high-performance clients to participate in federated learning and improve the accuracy and energy efficiency of the federated learning model.

Improving Interventions on Cardiovascular Disease and Diet: A Policy Incentive Analysis

Various Cardiovascular Diseases (CVDs) can be catastrophic and can lead to irreversible outcomes. Despite improved interventions for CVD prevention awareness, there continues to be discussion and research on diet-related CVD and mortality without addressing the problem. Instead of prioritizing public guidelines and policies, policymakers should understand CVD and address population barriers to adhering to a healthy diet that decreases CVD risk. Therefore, this project aims to analyze federal healthy food incentive policies to promote healthy diet behaviors that reduce CVD risk. The method used was existing data for a comparative policy analysis that included a policy proposal process: phases of progression, measures, and a policy model with data collection and requirements. This analysis compared a current federal food incentive program versus the proposed program. Results of the final analysis derived from the literature review and collected data stated consuming foods from the Mediterranean and other low-fat and low-salt diets reduced CVD risks that also reduced other risks secondary to CVD, such as obesity, diabetes, and Cerebrovascular Accident (CVA). Comparatively, combined healthy food incentives and disincentives were more effective for improving healthy behaviors than, in some cases, even after incentives were removed. Therefore, this policy analysis supports the indication for incentive policy change. However, the lack of federal stakeholders’ response to key policy changes upon proposal submission may require other methods of proposal dissemination. Nonetheless, focusing analysis on various Food Insecurity Nutrition Incentive (FINI) programs instead of one, multi-state program, which may have improved analysis outcomes, was the lesson learned.

An in-situ self-etching enabled high-power electrode for aqueous zinc-ion batteries

Sluggish storage kinetics is considered as the main bottleneck of cathode materials for fast-charging aqueous zinc-ion batteries(AZIBs).In this report,we propose a novel in-situ self-etching strategy to unlock the Palm tree-like vanadium oxide/carbon nanofiber membrane(P-VO/C)as a robust freestanding electrode.Comprehensive investigations including the finite element simulation,in-situ X-ray diffraction,and in-situ electrochemical impedance spectroscopy disclosed it an electrochemically induced phase transformation mechanism from VO to layered Zn_(x)V_(2)O_5·nH_(2)O,as well as superior storage kinetics with ultrahigh pseudocapacitive contribution.As demonstrated,such electrode can remain a specific capacity of 285 mA h g^(-1)after 100 cycles at 1 A g^(-1),144.4 mA h g^(-1)after 1500 cycles at 30 A g^(-1),and even 97 mA h g^(-1)after 3000 cycles at 60 A g^(-1),respectively.Unexpectedly,an impressive power density of 78.9 kW kg^(-1)at the super-high current density of 100 A g^(-1)also can be achieved.Such design concept of in-situ self-etching free-standing electrode can provide a brand-new insight into extending the pseudocapacitive storage limit,so as to promote the development of high-power energy storage devices including but not limited to AZIBs.

On Incentive and Coordination Mechanism of Service Outsourcing Based on Principal-Agent Theory and Blockchain Technology

To address the issue of information asymmetry between the two parties and moral hazard among service providers in the process of service outsourcing,this paper builds the Stackelberg game model based on the principal-agent framework,examines the dynamic game situation before the contract being signed,and develops four information models.The analysis reveals a Pareto improvement in the game’s Nash equilibrium when comparing the four models from the standpoint of the supply chain.In the complete information scenario,the service level of the service provider,the customer company’s incentive effectiveness,and the supply chain system’s ultimate profit are all maximized.Furthermore,a coordinating mechanism for disposable profit is built in this study.The paper then suggests a blockchain-based architecture for the service outsourcing process supervision and a distributed incentive mechanism under the coordination mechanism in response to the inadequacy of the principal-agent theory to address the information asymmetry problem and the moral hazard problem.The experiment’s end findings demonstrate that both parties can benefit from the coordination mechanism,and the application of blockchain technology can resolve these issues and effectively encourage service providers.

Research on the Incentive System of Human Resource Management in Universities

Based on the actual situation of the establishment of the incentive system for human resource management in universities,the constituent elements and relevant principles of the incentive system for human resources in universities are expounded on,the current situation of the actual needs of the faculty and staff in universities is studied and analyzed,and practical plans for establishing the concept and implementing the incentive system in universities are proposed,with relevant incentive mechanisms for human resource management focusing on differentiated needs developed for reference.

Research on Incentive Policies for Chinese Innovative Drug R&D - Taking Innovative Anti-tumor Drugs as an Example

Objective To provide reference for improving Chinese innovative drug research and development incentive policies.Methods Based on investigating the incentive policies for innovative drug research and development in clinical research,evaluation and approval in China,anti-tumor drugs were taken as the research object to discuss relevant policies from the perspective of clinical trials and registration approval based on data statistics and current situation analysis.Results and Conclusion Driven by a series of incentive policies for innovative drug R&D,great achievements have been made on anti-tumor drugs.However,there are problems such as concentration of drug targets,homogenization of clinical trials,and gaps in some drugs with large clinical needs.To improve incentive policies for innovative drug R&D,China should adhere to the orientation of clinical value,focusing on basic research and translational research,improving evaluation and approval capabilities,and establishing a sound ecosystem for innovative drugs.

Incentive mechanism analysis of information security outsourcing based on principal-agent model

In order to solve principal-agent problems caused by interest inconformity and information asymmetry during information security outsourcing, it is necessary to design a reasonable incentive mechanism to promote client enterprises to complete outsourcing service actively. The incentive mechanism model of information security outsourcing is designed based on the principal-agent theory. Through analyzing the factors such as enterprise information assets value, invasion probability, information security environment, the agent cost coefficient and agency risk preference degree how to impact on the incentive mechanism, conclusions show that an enterprise information assets value and invasion probability have a positive influence on the fixed fee and the compensation coefficient; while information security environment, the agent cost coefficient and agency risk preference degree have a negative influence on the compensation coefficient. Therefore, the principal enterprises should reasonably design the fixed fee and the compensation coefficient to encourage information security outsourcing agency enterprises to the full extent.

RTRC:A Reputation-Based Incentive Game Model for Trustworthy Crowdsourcing Service

The ubiquity of mobile devices have promoted the prosperity of mobile crowd systems, which recruit crowds to contribute their resources for performing tasks. Yet, due to the various resource consumption, the crowds may be reluctant to join and contribute information. Thus, the low participation level of crowds will be a hurdle that prevents the adoption of crowdsourcing. A critical challenge for these systems is how to design a proper mechanism such that the crowds spontaneously act as suppliers to contribute accurate information. Most of existing mechanisms ignore either the honesty of crowds or requesters respectively. In this paper, considering the honesty of both, we propose a game-based incentive mechanism, namely RTRC, to stimulate the crowds to contribute accurate information and to motivate the requesters to return accurate feedbacks. In addition, an evolutionary game is designed to model the dynamic of user-strategy selection. Specially, the replicator dynamic is applied to model the adaptation of strategy interactions taking into account the dynamic nature in time dependence and we also derive the evolutionarily stable strategies(ESSs) for users. Finally, empirical results over the simulations show that all the requesters and suppliers will select honest strategy to maximize their profit.

A Study on the Multi-Task Incentive Mechanism for the Service Provider

Both conflict and asymmetric information exist betweenthe telecom operators and the service provider,and result in illegal behaviors of the service provider.The relationship between the telecom operators andthe service provider is classical multi-task principalagentrelationship. The multi-task incentive for theservice provider is considered in the design of theprincipal-agent incentive contract, and it is necessaryto add the multi-task incentive to the serviceproviders through the analysis of the risk costs andthe agency costs of this problem.

Incentive Scheme for Slice Cooperation Based on D2D Communication in 5G Networks

In the 5th generation(5G)wireless communication networks,network slicing emerges where network operators(NPs)form isolated logical slices by the same cellular network infrastructure and spectrum resource.In coverage regions of access points(APs)shared by slices,device to device(D2D)communication can occur among different slices,i.e.,one device acts as D2D relay for another device serving by a different slice,which is defined as slice cooperation in this paper.Since selfish slices will not help other slices by cooperation voluntarily and unconditionally,this paper designs a novel resource allocation scheme to stimulate slice cooperation.The main idea is to encourage slice to perform cooperation for other slices by rewarding it with higher throughput.The proposed incentive scheme for slice cooperation is formulated by an optimal problem,where cooperative activities are introduced to the objective function.Since optimal solutions of the formulated problem are long term statistics,though can be obtained,a practical online slice scheduling algorithm is designed,which can obtain optimal solutions of the formulated maximal problem.Lastly,the throughput isolation indexes are defined to evaluate isolation performance of slice.According to simulation results,the proposed incentive scheme for slice cooperation can stimulate slice cooperation effectively,and the isolation of slice is also simulated and discussed.

Patient attitudes about financial incentives for diabetes self-management:A survey

AIM:To study the acceptability of incentives for behavior changes in individuals with diabetes,comparing financial incentives to self-rewards and non-financial incentives.METHODS:A national online survey of United States adults with diabetes was conducted in March 2013(n = 153).This survey was designed for this study,with iterative testing and modifications in a pilot population.We measured the demographics of individuals,their interest in incentives,as well as the perceived challenge of diabetes self-management tasks,and expectations of incentives to improve diabetes self-management(financial,non-financial and self-rewards).Using an ordered logistic regression model,we assessed the association between a 32-point score of the perceived challenge of the self-management tasks and the three types of rewards.RESULTS:Ninety-six percent of individuals were interested in financial incentives,60% in non-financial incentives and 72% in self-rewards.Patients were less likely to use financial incentives when they perceived the behavior to be more challenging(odds ratio of using financial incentives of 0.82(95%CI:0.72-0.93) for each point of the behavior score).While the effectiveness of incentives may vary according to the perceived level of challenge of each behavior,participants did not expect to need large amounts to motivate them to modify their behavior.The expected average amounts needed to motivate a 5 lb weight loss in our population and to maintain this weight change for a year was $258(interquartile range of $10-100) and $713(interquartile range of $25-250) for a 15 lb weight loss.The difference in mean amount estimates for 5 lb and 15 lb weight loss was significant(P < 0.001).CONCLUSION:Individuals with diabetes are willing to consider financial incentives to improve diabetes selfmanagement.Future studies are needed to explore incentive programs and their effectiveness for diabetes.

More children nutrition distribution, less labor incentive: Evidence from Chinese collective agriculture

Distribution system will affect the labor incentive that has been heatedly discussed by recent literatures.Using a unique micro dataset, this paper dem on strates that the equalitaria n distributi on system is one of the reas ons for the in sufficie nt labor incentive during the Chinese Collective Agriculture period. Specifically speaking, in the distribution of basic rations, the proportion for children (aged 1-3 and 4-7 years) was often beyond their nutrition demand, resulting the dissatisfaction of other families with more laborers and less children, thus these households will reduce their labor supply gradually. At the same time, the existence of outstanding accounts makes it a failure to use work points to buy distributions, which is the mechanism of the distribution system and insufficient labor incentive. All the results have been accepted by the robustness tests. The study will help to understand the distribution system and labor incentive, as well as the failure of the Chinese collective agriculture.

E-Supply Chain Incentive and Coordination Based on Revenue Sharing Contract

This paper took the buyer-biased electronic market as an example, where multiple suppliers selling short-life-cycle products are bidding for an order from a powerful buyer with stochastic customer demand. It used a single period newsvendor model to analyze the decision of supplied and buyers to do or not do business online. The results suggest that lack of Incentive is the key factor of B2B electronic markets failure. At the same time, it designed a revenue sharing contract to coordinate the E-supply chain in order to prevent failure of E-market.