Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning

Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. In federated learning, participants cooperatively train a global model, and they will receive the global model and payments. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their data quality. Furthermore, federated learning benefits from the cooperative contributions of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes stable cooperation has become an important issue to consider. In this paper, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. However, this mechanism is computationally expensive because it requires aggregating exponential models for all possible coalitions, which is infeasible in federated learning. To address this, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments verify that the efficient core-selecting mechanism can incentivize inputting high-quality data and stable cooperation, while it reduces computational overhead compared to the core-selecting mechanism

Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models

A Mechanism Design Approach to Bandwidth Allocation in Tactical Data Networks

The defense sector is undergoing a phase of rapid technological advancement, in the pursuit of its goal of information superiority. This goal depends on a large network of complex interconnected systems - sensors, weapons, soldiers - linked through a maze of heterogeneous networks. The sheer scale and size of these networks prompt behaviors that go beyond conglomerations of systems or `system-of-systems\u27. The lack of a central locus and disjointed, competing interests among large clusters of systems makes this characteristic of an Ultra Large Scale (ULS) system. These traits of ULS systems challenge and undermine the fundamental assumptions of today\u27s software and system engineering approaches. In the absence of a centralized controller it is likely that system users may behave opportunistically to meet their local mission requirements, rather than the objectives of the system as a whole. In these settings, methods and tools based on economics and game theory (like Mechanism Design) are likely to play an important role in achieving globally optimal behavior, when the participants behave selfishly. Against this background, this thesis explores the potential of using computational mechanisms to govern the behavior of ultra-large-scale systems and achieve an optimal allocation of constrained computational resources Our research focusses on improving the quality and accuracy of the common operating picture through the efficient allocation of bandwidth in tactical data networks among self-interested actors, who may resort to strategic behavior dictated by self-interest. This research problem presents the kind of challenges we anticipate when we have to deal with ULS systems and, by addressing this problem, we hope to develop a methodology which will be applicable for ULS system of the future. We build upon the previous works which investigate the application of auction-based mechanism design to dynamic, performance-critical and resource-constrained systems of interest to the defense community. In this thesis, we consider a scenario where a number of military platforms have been tasked with the goal of detecting and tracking targets. The sensors onboard a military platform have a partial and inaccurate view of the operating picture and need to make use of data transmitted from neighboring sensors in order to improve the accuracy of their own measurements. The communication takes place over tactical data networks with scarce bandwidth. The problem is compounded by the possibility that the local goals of military platforms might not be aligned with the global system goal. Such a scenario might occur in multi-flag, multi-platform military exercises, where the military commanders of each platform are more concerned with the well-being of their own platform over others. Therefore there is a need to design a mechanism that efficiently allocates the flow of data within the network to ensure that the resulting global performance maximizes the information gain of the entire system, despite the self-interested actions of the individual actors. We propose a two-stage mechanism based on modified strictly-proper scoring rules, with unknown costs, whereby multiple sensor platforms can provide estimates of limited precisions and the center does not have to rely on knowledge of the actual outcome when calculating payments. In particular, our work emphasizes the importance of applying robust optimization techniques to deal with the uncertainty in the operating environment. We apply our robust optimization - based scoring rules algorithm to an agent-based model framework of the combat tactical data network, and analyze the results obtained. Through the work we hope to demonstrate how mechanism design, perched at the intersection of game theory and microeconomics, is aptly suited to address one set of challenges of the ULS system paradigm - challenges not amenable to traditional system engineering approaches

Electric vehicle as a service (EVaaS):applications, challenges and enablers

Under the vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed as loads to absorb excess production or as distributed energy resources to supply part of their stored energy back to the grid. This paper overviews the technologies, technical components and system requirements needed for EV deployment. Electric vehicle as a service (EVaaS) exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. The EVaaS framework is introduced, and interactions among EVaaS subsystems such as EV batteries, charging stations, loads and advanced metering infrastructure are studied. The communication infrastructure and processing facilities that enable data and information exchange between EVs and the grid are reviewed. Different strategies for EV charging/discharging and their impact on the distribution grid are reviewed. Several market designs that incentivize energy trading in V2G environments are discussed. The benefits of V2G are studied from the perspectives of ancillary services, supporting of renewables and the environment. The challenges to V2G are studied with respect to battery degradation, energy conversion losses and effects on distribution system

Mechanism design and game theoretical models for intrusion detection

In this thesis, we study the problems related to intrusion detection systems in Mobile Ad hoc Networks (MANETs). Specifically, we are addressing the leader election in the presence of selfish nodes, the tradeoff between security and IDS's resource consumption, and the multi-fragment intrusion detection via sampling. To balance the resource consumption among all the nodes and prolong the lifetime of a MANET, the nodes with the most remaining resources should be elected as the leaders. Selfishness is one of the main problems facing such a model where nodes can behave selfishly during the election or after. To address this issue, we present a solution based on the theory of mechanism design. More specifically, the solution provides nodes with incentives in the form of reputations to encourage nodes in participating honestly in the election process. The amount of incentives is based on the Vickrey-Clarke-Groves (VCG) mechanism to ensure that truth-telling is the dominant strategy of any node. To catch and punish a misbehaving elected leader, checkers are selected randomly to monitor the behavior of a leader. To reduce the false-positive rate, a cooperative game-theoretic model is proposed to analyze the contribution of each checker on the catch decision. A multi-stage catch mechanism is also introduced to reduce the performance overhead of checkers. Additionally, we propose a series of local election algorithms that lead to globally optimal election results. Note that the leader election model, which is known as moderate model is only suitable when the probability of attacks is low. Once the probability of attacks is high, victims should launch their own IDSs. Such a robust model is, however, costly with respect to energy, which leads nodes to die fast. Clearly, to reduce the resource consumption of IDSs and yet keep its effectiveness, a critical issue is: When should we shift from moderate to robust mode? Here, we formalize this issue as a nonzero-sum non-cooperative game-theoretical model that takes into consideration the tradeoff between security and IDS resource consumption. Last but not least, we consider the problem of detecting multi-fragments intrusions that are launched from a MANET targeting another network. To generalize our solution, we consider the intrusion to be launched from any type of networks. The detection is accomplished by sampling a subset of the transmitted packets over selected network links or router interfaces. Given a sampling budget, our framework aims at developing a network packet sampling strategy to effectively reduce the success chances of an intruder. Non-cooperative game theory is used to express the problem formally. Finally, empirical results are provided to support our solutions

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

Anonymizing and Trading Person-specific Data with Trust

In the past decade, data privacy, security, and trustworthiness have gained tremendous attention from research communities, and these are still active areas of research with the proliferation of cloud services and social media applications. The data is growing at a rapid pace. It has become an integral part of almost every industry and business, including commercial and non-profit organizations. It often contains person-specific information and a data custodian who holds it must be responsible for managing its use, disclosure, accuracy and privacy protection. In this thesis, we present three research problems. The first two problems address the concerns of stakeholders on privacy protection, data trustworthiness, and profit distribution in the online market for trading person-specific data. The third problem addresses the health information custodians (HICs) concern on privacy-preserving healthcare network data publishing. Our first research problem is identified in cloud-based data integration service where data providers collaborate with their trading partners in order to deliver quality data mining services. Data-as-a-Service (DaaS) enables data integration to serve the demands of data consumers. Data providers face challenges not only to protect private data over the cloud but also to legally adhere to privacy compliance rules when trading person-specific data. We propose a model that allows the collaboration of multiple data providers for integrating their data and derives the contribution of each data provider by valuating the incorporated cost factors. This model serves as a guide for business decision-making, such as estimating the potential privacy risk and finding the sub-optimal value for publishing mashup data. Experiments on real-life data demonstrate that our approach can identify the sub-optimal value in data mashup for different privacy models, including K-anonymity, LKC-privacy, and ϵ-differential privacy, with various anonymization algorithms and privacy parameters. Second, consumers demand a good quality of data for accurate analysis and effective decision- making while the data providers intend to maximize their profits by competing with peer providers. In addition, the data providers or custodians must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a two-fold solution: (1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semi-trusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup, and (2) we incorporate the Vickrey-Clarke-Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements. Finally, we address the concerns of HICs of exchanging healthcare data to provide better and more timely services while mitigating the risk of exposing patients’ sensitive information to privacy threats. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet, an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets