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.

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.

Carbon Abatement Cost-Sharing Strategy for Electric Power Sector Based on Incentive and Subsidy Mechanisms

The green and low carbon transition and development of the electricity industry is the most crucial task in realizing the“dual-carbon target”,and it is urgent to explore the incentive and subsidy mechanism to promote green electricity consumption and the cost-sharing strategy of carbon reduction,to alleviate the pressure of carbon abatement cost of each subject of the electricity supply chain.Against this background,this paper takes into account the low-carbon subsidies provided by the government and the incentive subsidies for users,and studies the optimal decision-making of each subject in the electricity supply chain,so that each of them can obtain the optimal profit and achieve carbon emission reduction at the same time.Firstly,taking into account the direct power purchase mode of large users and the electricity-selling companies emerging after the reform of the power sales side,we have established a cooperative mechanism for sharing the cost of carbon emission reduction in the electricity supply chain and clarified the relationship between the supply and demand of electricity among the main parties.Subsequently,considering government low-carbon subsidies and user incentive subsidies,the optimal decisionmaking model is established under two scenarios of decentralized and centralized cooperative games in the supply chain,respectively,with the objective of maximizing profits and carbon reduction rates.Solving for the optimal proportion of carbon abatement costs shared by each participant in the electricity supply chain in achieving game equilibrium.Finally,we analyze the role of the government’s low-carbon subsidies,users’incentive subsidies,and other factors on the profit and carbon reduction effect of the electricity industry through the example analysis and further analyze the impact of carbon abatement cost-sharing measures to provide recommendations for the electricity industry to realize low-carbon abatement and make decisions.

Stackelberg Game-Based Optimal Dispatch for PEDF Park and Power Grid Interaction under Multiple Incentive Mechanisms

The integration of photovoltaic,energy storage,direct current,and flexible load(PEDF)technologies in building power systems is an importantmeans to address the energy crisis and promote the development of green buildings.The friendly interaction between the PEDF systems and the power grid can promote the utilization of renewable energy and enhance the stability of the power grid.For this purpose,this work introduces a framework of multiple incentive mechanisms for a PEDF park,a building energy system that implements PEDF technologies.The incentive mechanisms proposed in this paper include both economic and noneconomic aspects,which is the most significant innovation of this paper.By modeling the relationship between a PEDF park and the power grid into a Stackelberg game,we demonstrate the effectiveness of these incentive measures in promoting the friendly interaction between the two entities.In this game model,the power grid determines on the prices of electricity trading and incentive subsidy,aiming to maximize its revenue while reducing the peak load of the PEDF park.On the other hand,the PEDF park make its dispatch plan according to the prices established by the grid,in order to reduce electricity consumption expense,improve electricity utility,and enhance the penetration rate of renewable energy.The results show that the proposed incentive mechanisms for the PEDF park can help to optimize energy consumption and promote sustainable energy practices.

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.

Research on the Improvement of Incentive Mechanism for Pharmaceutical Sales Personnel - Taking Company A as an Example

Objective To analyze the improvement of the incentive mechanism of sales personnel in pharmaceutical company A,and to promote the smooth operation and further development of the company in a long term.Methods Compensation incentive,performance appraisal,welfare benefit,training incentive,promotion motivation and enterprise cultural inspiration were explored through questionnaires,telephone interviews and in-person interviews.Results and Conclusion This company’s incentive mechanism has problems in two aspects:Material incentives and spiritual incentives.As to the company’s characteristics and strategic development,the optimization countermeasures of incentive mechanism are proposed from the following three aspects:constructing a reasonable incentive system,establishing an efficient spiritual incentive mechanism,and implementing the dynamic incentive and differentiated incentive simultaneously.

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.

Application and management of continuous glucose monitoring in diabetic kidney disease

Diabetic kidney disease(DKD)is a common complication of diabetes mellitus that contributes to the risk of end-stage kidney disease(ESKD).Wide glycemic var-iations,such as hypoglycemia and hyperglycemia,are broadly found in diabetic patients with DKD and especially ESKD,as a result of impaired renal metabolism.It is essential to monitor glycemia for effective management of DKD.Hemoglobin A1c(HbA1c)has long been considered as the gold standard for monitoring glycemia for>3 months.However,assessment of HbA1c has some bias as it is susceptible to factors such as anemia and liver or kidney dysfunction.Continuous glucose monitoring(CGM)has provided new insights on glycemic assessment and management.CGM directly measures glucose level in interstitial fluid,reports real-time or retrospective glucose concentration,and provides multiple glycemic metrics.It avoids the pitfalls of HbA1c in some contexts,and may serve as a precise alternative to estimation of mean glucose and glycemic variability.Emerging studies have demonstrated the merits of CGM for precise monitoring,which allows fine-tuning of glycemic management in diabetic patients.Therefore,CGM technology has the potential for better glycemic monitoring in DKD patients.More research is needed to explore its application and management in different stages of DKD,including hemodialysis,peritoneal dialysis and kidney transplantation.

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.

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis(TB),especially multidrug-resistant TB and extensively drug-resistant TB,has strongly motivated the research and development of new anti-TB drugs.New strategies to facilitate drug combinations,including pharmacokinetics-guided dose optimization and toxicology studies of first-and second-line anti-TB drugs have also been introduced and recommended.Liquid chromatography-mass spectrometry(LC-MS)has arguably become the gold standard in the analysis of both endo-and exo-genous compounds.This technique has been applied successfully not only for therapeutic drug monitoring(TDM)but also for pharmacometabolomics analysis.TDM improves the effectiveness of treatment,reduces adverse drug reactions,and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window.Based on TDM,the dose would be optimized individually to achieve favorable outcomes.Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs,aiding in the discovery of potential biomarkers for TB diagnostics,treatment monitoring,and outcome evaluation.This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades.Besides,we discussed the advantages and disadvantages of this technique in practical use.The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted.Lastly,we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies(pharmacometrics,drug and vaccine developments,machine learning/artificial intelligence,among others)to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Fiber optic monitoring of an anti-slide pile in a retrogressive landslide

Anti-slide piles are one of the most important reinforcement structures against landslides,and evalu-ating the working conditions is of great significance for landslide mitigation.The widely adopted analytical methods of pile internal forces include cantilever beam method and elastic foundation beam method.However,due to many assumptions involved in calculation,the analytical models cannot be fully applicable to complex site situations,e.g.landslides with multi-sliding surfaces and pile-soil interface separation as discussed herein.In view of this,the combination of distributed fiber optic sensing(DFOS)and strain-internal force conversion methods was proposed to evaluate the working conditions of an anti-sliding pile in a typical retrogressive landslide in the Three Gorges reservoir area,China.Brillouin optical time domain reflectometry(BOTDR)was utilized to monitor the strain distri-bution along the pile.Next,by analyzing the relative deformation between the pile and its adjacent inclinometer,the pile-soil interface separation was profiled.Finally,the internal forces of the anti-slide pile were derived based on the strain-internal force conversion method.According to the ratio of calculated internal forces to the design values,the working conditions of the anti-slide pile could be evaluated.The results demonstrated that the proposed method could reveal the deformation pattern of the anti-slide pile system,and can quantitatively evaluate its working conditions.

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.

Improved Flexible Triboelectric Nanogenerator Based on Tile-Nanostructure for Wireless Human Health Monitor

Triboelectric nanogenerators(TENGs)have emerged as promising candidates for integrating with flexible electronics as self-powered systems owing to their intrinsic flexibility,biocompatibility,and miniaturization.In this study,an improved flexible TENG with a tile-nanostructured MXene/polymethyl methacrylate(PMMA)composite electrode(MP-TENG)is proposed for use in wireless human health monitor.The multifunctional tile-nanostructured MXene/PMMA film,which is self-assembled through vacuum filtration,exhibits good conductivity,excellent charge capacity,and high flexibility.Thus,the MXene/PMMA composite electrode can simultaneously function as a charge-generating,charge-trapping,and charge-collecting layer.Furthermore,the charge-trapping capacity of a tile nanostructure can be optimized on the basis of the PMMA concentration.At a mass fraction of 4%PMMA,the MP-TENG achieves the optimal output performance,with an output voltage of 37.8 V,an output current of 1.8μA,and transferred charge of 14.1 nC.The output power is enhanced over twofold compared with the pure MXene-based TENG.Moreover,the MP-TENG has sufficient power capacity and durability to power small electronic devices.Finally,a wireless human motion monitor based on the MP-TENG is utilized to detect physiological signals in various kinematic motions.Consequently,the proposed performance-enhanced MP-TENG proves a considerable potential for use in health monitoring,telemedicine,and self-powered systems.

Application of GNSS-PPP on Dynamic Deformation Monitoring of Offshore Platforms

The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has been considered for this purpose,its monitoring accuracy is relatively low.Moreover,the influence of background noise on the dynamic monitoring accuracy of GNSS-PPP remains unclear.Hence,it is imperative to further validate the feasibility of GNSS-PPP for deformation monitoring of offshore platforms.To address these concerns,vibration table tests with different amplitudes and frequencies are conducted.The results demonstrate that GNSS-PPP can effectively monitor horizontal vibration displacement as low as±30 mm,which is consistent with GNSS-RTK.Furthermore,the spectral characteristic of background noise in GNSS-PPP is similar to that of GNSS-RTK(Real Time Kinematic).Building on this observation,an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)has been proposed to de-noise the data and enhance the dynamic monitoring accuracy of GNSS-PPP.Field monitoring application research is also undertaken,successfully extracting and analyzing the dynamic deformation of an offshore platform structure under environmental excitation using GNSS-PPP monitoring in conjunction with improved CEEMDAN de-noising.By comparing the de-noised dynamic deformation trajectories of the offshore platform during different periods,it is observed that the platform exhibits reversible alternating vibration responses under environmental excitation,with more pronounced displacement deformation in the direction of load action.The research results confirm the feasibility and potential of GNSS-PPP for dynamic deformation monitoring of offshore platforms.

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.

Deformation monitoring of long-span railway bridges based on SBAS-InSAR technology

The deformation monitoring of long-span railway bridges is significant to ensure the safety of human life and property.The interferometric synthetic aperture radar(In SAR)technology has the advantage of high accuracy in bridge deformation monitoring.This study monitored the deformation of the Ganjiang Super Bridge based on the small baseline subsets(SBAS)In SAR technology and Sentinel-1A data.We analyzed the deformation results combined with bridge structure,temperature,and riverbed sediment scouring.The results are as follows:(1)The Ganjiang Super Bridge area is stable overall,with deformation rates ranging from-15.6 mm/yr to 10.7 mm/yr(2)The settlement of the Ganjiang Super Bridge deck gradually increases from the bridge tower toward the main span,which conforms to the typical deformation pattern of a cable-stayed bridge.(3)The sediment scouring from the riverbed cause the serious settlement on the bridge’s east side compared with that on the west side.(4)The bridge deformation negatively correlates with temperature,with a faster settlement at a higher temperature and a slow rebound trend at a lower temperature.The study findings can provide scientific data support for the health monitoring of long-span railway bridges.

Thermally Conductive and UV-EMI Shielding Electronic Textiles for Unrestricted and Multifaceted Health Monitoring

Skin-attachable electronics have garnered considerable research attention in health monitoring and artificial intelligence domains,whereas susceptibility to elec-tromagnetic interference(EMI),heat accumulation issues,and ultraviolet(UV)-induced aging problems pose significant constraints on their potential applications.Here,an ultra-elas-tic,highly breathable,and thermal-comfortable epidermal sensor with exceptional UV-EMI shielding performance and remarkable thermal conductivity is developed for high-fidelity monitoring of multiple human electrophysiological signals.Via filling the elastomeric microfibers with thermally conductive boron nitride nanoparticles and bridging the insulating fiber interfaces by plating Ag nanoparticles(NPs),an interwoven thermal con-ducting fiber network(0.72 W m^(-1) K^(-1))is constructed benefiting from the seamless thermal interfaces,facilitating unimpeded heat dissipation for comfort skin wearing.More excitingly,the elastomeric fiber substrates simultaneously achieve outstanding UV protection(UPF=143.1)and EMI shielding(SET>65,X-band)capabilities owing to the high electrical conductivity and surface plasmon resonance of Ag NPs.Furthermore,an electronic textile prepared by printing liquid metal on the UV-EMI shielding and thermally conductive nonwoven textile is finally utilized as an advanced epidermal sensor,which succeeds in monitoring different electrophysiological signals under vigorous electromagnetic interference.This research paves the way for developing protective and environmentally adaptive epidermal electronics for next-generation health regulation.

Monitoring seismicity in the southern Sichuan Basin using a machine learning workflow

Monitoring seismicity in real time provides significant benefits for timely earthquake warning and analyses.In this study,we propose an automatic workflow based on machine learning(ML)to monitor seismicity in the southern Sichuan Basin of China.This workflow includes coherent event detection,phase picking,and earthquake location using three-component data from a seismic network.By combining Phase Net,we develop an ML-based earthquake location model called Phase Loc,to conduct real-time monitoring of the local seismicity.The approach allows us to use synthetic samples covering the entire study area to train Phase Loc,addressing the problems of insufficient data samples,imbalanced data distribution,and unreliable labels when training with observed data.We apply the trained model to observed data recorded in the southern Sichuan Basin,China,between September 2018 and March 2019.The results show that the average differences in latitude,longitude,and depth are 5.7 km,6.1 km,and 2 km,respectively,compared to the reference catalog.Phase Loc combines all available phase information to make fast and reliable predictions,even if only a few phases are detected and picked.The proposed workflow may help real-time seismic monitoring in other regions as well.

Natural Disaster Risk Monitoring for Immovable Cultural Relics Based on Digital Twin

Natural disaster risk monitoring is an important task for disaster prevention and reduction.In the case of immovable cultural relics,however,the feedback mechanism,risk factors,monitoring logic,and monitoring indicators of natural disaster risk monitoring are complex.How to achieve intelligent perception and monitoring of natural disaster risk for immovable cultural relics has always been a focus and a challenge for researchers.Based on the analysis of the concepts and issues related to the natural disaster risk of immovable cultural relics,this paper proposes a framework for natural disaster risk monitoring for immovable cultural relics based on the digital twin.This framework focuses on risk monitoring,including the physical entities of natural disaster risk for immovable cultural relics,monitoring indicators,and virtual entity construction.A platform for monitoring the natural disaster risk of immovable cultural relics is proposed.Using the Puzhou Ancient City Site as a test bed,the proposed concept can be used for monitoring the natural disaster risk of immovable cultural relics at different scales.