With the flourishing of the Agricultural Internet of Things (AIoT), analyzing large-volume sensor data has become a regular requirement for agricultural decision-making. Federated learning (FL), which facilitates scattered AIoT devices to train models collaboratively, has gained significant attention. However, traditional FL poses challenges in AIoT scenarios, such as wide geo-distribution, heterogeneous data distribution, and high-device risks. Existing works tend to be one-sided and remain unclear on how to tackle these issues thoroughly in AIoT. To fill the gap, we presentBc(2)FL, a double-layer blockchain-based FL framework, which enhances both learning efficiency and security for AIoT. The double-layer blockchain, coupled with a two-stage consensus algorithm, drives the hierarchical FL process to enable efficient and reliable agricultural knowledge-sharing. In addition, Bc(2)FL adopts an adaptive model aggregation algorithm to dynamically tune noise levels based on the model quality, further improving the learning security and model credibility. Finally, the extensive experimental results demonstrate that Bc(2)FL not only improves the model accuracy by up to 21.17% compared with the state-of-the-art baselines, but also enhances the privacy protection within an additional error of only 2.1%.

With the popularization of artificial intelligence-generated content (AIGC), the explosion of end users is spurring an unprecedented diversity in preferences. To cope with this challenge, the AIGC paradigm is shifting from cloud-driven pre-trained large models to edge-driven personalized customization models. The former are large-scale models with more than 100 billion parameters trained based on massive data, while the latter introduce heterogeneous user preferences or professional knowledge to fine-tune the former. However, such fine-tuning incurs costly resource consumption and privacy disclosure. In this paper, we offer a holistic perspective on the AIGC fine-tuning framework spanning from end user to edge to cloud. Specifically, we first investigate how to deploy an edge-driven collaborative fine-tuning task through federated learning. Then, we discuss a verifiable model consensus protocol and fairness incentive design for edge servers to participate in a collaborative learning task. In addition, a case study focuses on the medical scenario, where we develop a practical X-ray diagnostic demo through collaborative fine-tuning of a multimodal pre-training model, and the diagnostic dialogue and performance results are compared. Finally, potential research directions are identified to advance the edge-driven customized AIGC services.

Hyperledger Fabric is one of the most popular permissioned blockchain platforms widely adopted in enterprise blockchain solutions. To optimize and fully utilize the platform, it is desired to conduct a thorough performance analysis of Hyperledger Fabric. Although numerous studies have analyzed the performance of Hyperledger Fabric, three significant limitations still exist. First, existing blockchain performance evaluation frameworks rely on fixed workload rates, which fail to accurately reflect the performance of blockchain systems in real-world application scenarios. Second, the impact of extending the breadth and depth of endorsement policies on the performance of blockchain systems has yet to be adequately studied. Finally, the impact of node crashes and recoveries on blockchain system performance has yet to be comprehensively investigated. To address these limitations, we propose a framework called BlockLoader, which offers seven different distributions of load rates, including linear, single-peak, and multi-peak patterns. Next, we employ the BlockLoader framework to analyze the impact of endorsement policy breadth and depth on blockchain performance, both qualitatively and quantitatively. Additionally, we investigate the impact of dynamic node changes on performance. The experimental results demonstrate that different endorsement policies exert distinct effects on performance regarding breadth and depth scalability. In the horizontal expansion of endorsement policies, the OR endorsement policy demonstrates stable performance, fluctuating around 88 TPS, indicating that adding organizations and nodes has minimal impact. In contrast, the AND endorsement policy exhibits a declining trend in performance as the number of organizations and nodes increases, with an average decrease of 10 TPS for each additional organization. Moreover, the dynamic behaviour of nodes exerts varying impacts across these endorsement policies. Specifically, under the AND endorsement policy, dynamic changes in nodes significantly affect system performance. The TPS of the AND endorsement policy shows a notable decline, dropping from 79.6 at 100 s to 41.96 at 500 s, reflecting a reduction of approximately 47% over time. Under the OR endorsement policy, the system performance remains almost unaffected.

As a fundamental component of Internet of Things (IoT) devices, firmware plays an essential role. Nowadays, the development of IoT firmware relies extensively on third-party components and substantially enhances development efficiency. However, these components are not inherently secure, and their vulnerabilities can adversely affect the security of IoT firmware. Existing research adopts binary code similarity analysis to detect known vulnerabilities in firmware. However, it encounters significant challenges, primarily in extracting function features from the limited semantic information within binary code. Another challenge is the need for real-world datasets to assess the model's performance in practical scenarios, such as firmware supply chain analysis. We present a detection model named PDG2VEC based on Program Dependence Graphs (PDGs) to tackle these challenges. PDG2VEC extracts function features at the variable level on PDG and assesses function similarity by evaluating whether two functions can represent each other. We conducted evaluations using three datasets, including one we created to simulate a firmware supply chain scenario. The experimental results demonstrate that PDG2VEC exhibits resilience to cross-architecture challenges and captures more precise semantics than other approaches. Furthermore, PDG2VEC outperforms state-of-the-art tools in the supply chain analysis scenario, with a 16% higher AUC value average against baseline approaches.

Blockchain technology is widely used in the Internet of Things (IoT) with its decentralization, traceability, and tamper-resistant features. Unfortunately, the massive data volumes in IoT systems, combined with the prevalent full-replication strategy employed by conventional blockchain, exert considerable storage demands on IoT edge computing devices and present a formidable challenge for resource-constrained nodes to join blockchain networks. Although some existing solutions improve storage scalability by reducing the number of redundant copies, they still pose issues of high storage complexity and significant cross-tier query costs. In this paper, we first propose a multi-tier storage framework using erasure coding for blockchain, called MTEC, tailored for IoT scenarios with an exponentially decaying data access pattern. Specifically, MTEC employs Reed-Solomon(RS) coding to build a tiered blockchain storage framework. Each tier is equipped with an encoding scheme and threshold time to minimize storage overhead. To enhance query efficiency, we design a block pre-loading strategy and node collaborative query strategy. Finally, we implement a prototype of MTEC and compare it with baseline blockchain systems. Extensive experiments demonstrate that MTEC achieves a substantial 86.3

The blockchain can offer a dependable and secure platform for Internet of Things (IoT) transactions with its distributed and secure network architecture. Unfortunately, it faces challenges, such as limited throughput, excessive computational costs, and high-transaction fees. Off-chain scaling protocols are used to address the scalability of blockchain for their outstanding performance and efficiency. To mitigate the high-cost interactions with blockchain, previous studies only considered moving transactions of payment hubs (PHs) off-chain, utilizing off-chain operators to aggregate multiple transactions. However, existing PHs overly rely on central operators for system maintenance, greatly increasing the risk of central operator failure (COF). Previous solutions allowed operators to submit unsettled state commitments (USCs) to the blockchain and overlooked the pessimistic scenario that could lead to state rollbacks. To address these issues, this article proposes an efficient multiparty payment protocol (HyperPay), aimed at utilizing the off-chain scaling technique to enhance transaction throughput and reduce on-chain cost. Specifically, we first propose a novel off-chain committee and collateral-based verifiable random leader election (C-VRE) to elect leaders fairly, thus mitigating the COF problem. Additionally, we design a new state validation mechanism and one-step fraud challenge (OSFC), enabling verifiers to directly construct fraud proofs and challenges on-chain, thereby preventing leaders from submitting USC. Our evaluation indicates that HyperPay reduces on-chain costs of challenge by 80% and boosts peak throughput by a factor of 10X-283X. A comprehensive theoretical analysis and experimental results substantiate the security and effectiveness of our proposed approach.

The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in the Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility.

In cross-silo federated learning (FL), due to the heterogeneous participants, free-riders can utilize information asymmetry to make profits without performing any local model training. Free-riding attack poses possibilities and opportunities for unfairness and can seriously impair the operation of the FL ecosystem. It motivates our work to explore and characterize the unique features of free-riding attack, which differ from other attacks such as poisoning attacks. In this paper, we propose an evolutionary public goods game-based incentive model (Fed-EPG), which makes the first attempt to construct the interaction model among the participants through the evolutionary public goods game. Specifically, we consider both the public good characteristics of cross-silo FL models as well as the bounded rationality and incomplete information of competitors. We first introduce asymmetric environmental feedback to represent reward and punishment strategies in evolutionary game, and then adopt a multi-segment nonlinear control method to dynamically adjust the rewards and punishments among the participants, which achieves the incentive for the participants to cooperate stably during the training process. Experimental results validate that our incentive model is effective in the mitigation of free-riding. attacks.

Economic systems play pivotal roles in the metaverse. However, we have not yet found an overview that systematically introduces economic systems for the metaverse. Therefore, we review the state-of-the-art solutions, architectures, and systems related to economic systems. When investigating those state-of-the-art studies, we keep two questions in mind: (1) What is the framework of economic systems in the context of the metaverse? and (2) What activities would economic systems engage in the metaverse? This article aims to disclose insights into the economic systems that work for both the current and the future metaverse. To have a clear overview of the economic system framework, we mainly discuss the connections among three fundamental elements in the metaverse, i.e., digital creation, digital assets, and the digital trading market. After that, we elaborate on each topic of the proposed economic system framework. Those topics include incentive mechanisms, monetary systems, digital wallets, decentralized finance activities, and cross-platform interoperability for the metaverse. For each topic, we mainly discuss three questions: (a) the rationale of this topic, (b) why the metaverse needs this topic, and (c) how this topic will evolve in the metaverse. Through this overview, we wish readers can better understand what economic systems the metaverse needs and the insights behind the economic activities in the metaverse.

The Metaverse is established by twining a practical world in a virtual form, where users could become creators of learning-based user-generated content (UGC). However, the digital assets are not owned by the creators, which deviates from the decentralized Metaverse based on blockchain in Web 3.0. The contribution incentive design for UGC still faces the challenges of ownership verification and privacy protection. To this end, we propose minting the learning model into the non-fungible token (NFT) with federated learning (FL) assistance (referred to as FL-NFT), such that users as stakeholders can control the ownership and share the economic value of UGC. Specifically, the users are encouraged to establish a decentralized autonomous organization (DAO) to aggregate local models and mint FL-NFT. We formulate an auction interaction of FL-NFT as imperfect information Stackelberg game (IISG) to optimize the bidding strategies to realize individual rationality. Finally, we conduct simulations to show the effectiveness of the proposed scheme.

Federated learning (FL) has become a new form of data sharing by aggregating multi-party local models. Although the existing FL incentive system has reduced insufficient data supply under comprehensive information, it still confronts issues including free-riding, unfairness, and unreliability. Therefore, this paper proposes an incomplete information FL incentive mechanism based on blockchain and Bayesian games. The data transaction process is modeled by quantifying the cost-utility of the data providers and the payment reward of the data requesters, in which Shapley value is used to realize the fairness of reward distribution of data providers. We consider the heterogeneity and privacy protection of participating individuals. The data providers’ resource allocation strategies are built as a Bayesian game model, which optimizes the local training strategy to realize the incentive effect on the data providers. Furthermore, we consider the effectiveness of the incentive mechanism, a privacy-preserving Bayesian game action strategy consensus algorithm (PPBG-AC) is proposed, which enables the data providers to realize Bayesian Nash equilibrium under a data trading platform based on blockchain. The comparison and analysis of the schemes reveal that the incentive mechanism presented in our paper assures benefit distribution fairness and resource allocation credibility. Simulation experiments and performance evaluations based on real datasets demonstrate the effectiveness of our incentive mechanism.

基于联邦学习的智能边缘计算在物联网领域有广泛的应用前景.联邦学习是一种将数据存储在参与节点本地的分布式机器学习框架,可以有效保护智能边缘节点的数据隐私.现有的联邦学习通常将模型训练的中间参数上传至参数服务器实现模型聚合,此过程存在两方面问题:一是中间参数的隐私泄露,现有的隐私保护方案通常采用差分隐私给中间参数增加噪声,但过度加噪会降低聚合模型质量;另一方面,节点的自利性与完全自治化的训练过程可能导致恶意节点上传虚假参数或低质量模型,影响聚合过程与模型质量.基于此,本文将联邦学习中心化的参数服务器构建为去中心化的参数聚合链,利用区块链记录模型训练过程的中间参数作为证据,并激励协作节点进行模型参数验证,惩罚上传虚假参数或低质量模型的参与节点,以约束其自利性.此外,将模型质量作为评估依据,实现中间参数隐私噪声的动态调整以及自适应的模型聚合.原型搭建和仿真实验验证了模型的实用性,证实本模型不仅能增强联邦学习参与节点间的互信,而且能防止中间参数隐私泄露,从而实现隐私保护增强的可信联邦学习模型.

Federated learning is a distributed machine learning framework that enables distributed model training with local datasets, which can effectively protect the data privacy of workers (i.e., intelligent edge nodes). The majority of federated learning algorithms assume that the workers are trusted and voluntarily participate in the cooperative model training process. However, the situation in practical application is not consistent with this. There are many challenges such as worker selection schemes for participating workers, which hamper the widespread adoption of federated learning. The existing research about worker selection scheme focused on multi-weight subjective logic model to calculate reputation value and adopted contract theory to motivate workers, which may exist subjective judgmental factors and unfair profit distribution. To address above challenges, we calculate the reputation value by model quality parameters to evaluate the reliability of workers. Blockchain is designed to store historical reputation value that realized tamperresistance and non-repudiation. Numerical results indicate that the worker selection scheme can improve the accuracy of the model and accelerate the model convergence.

区块链采用密码学、共识算法、点对点通讯等技术构建了分布式的信任基础,实现链上数据的防篡改和可追溯等功能.区块链技术是金融科技领域的重要技术创新,已在数据共享、电子存证、消息溯源等领域应用,与此同时大规模节点通讯引发的性能和可扩展性等问题也限制了区块链应用的进一步发展.本文从区块链基本概念入手,分别对区块链中的关键技术,包括密码学与分布式账本、共识机制、智能合约、可扩展性技术等进行详细分析;介绍了区块链技术的主要应用,并指出区块链技术发展和应用中面临的挑战.

The concept of open banking has been a powerful trigger for the revolution in the financial services industry. When financial institutions disclose application programming interfaces (APIs) to third-party providers (TPPs), the biggest system risks concern issues such as malicious attack, data leakage and tampering, privacy disclosure and more. API is a new communication path for information systems, but it could be misused and tampered. To address this, we conceptualize a blockchain-based data sharing scheme for open banking named OBBC, in which the API’s information can be saved in a blockchain, that no one can dominate it. We propose an API consensus mechanism aims to ensures that the open API can’t be maliciously tampered. Moreover, zero knowledge proof and Merkel tree structure are used to realize that users’ privacy protection. In particular, we give the framework of our scheme and compare with existing data sharing schemes. We further implement a software prototype on fabric framework with real-world dataset. Experiment results show the feasibility, usability and scalability of our proposed open banking system.

Driven by technologies such as deep learning and blockchain, financial service industry has made rapid improvements in recent years. Mortgage loan is the main profit-making means of commercial banks, but there are many problems such as manpower cost, insufficient accuracy and evaluation of collateral. To cope with those problem, Artificial Intelligence (AI) and blockchain have been widely exploited. However, the monitoring of collateral is image recognition in dynamic video and it is difficult to avoid the risk of security attack. In this paper, we design a blockchain empowered method to achieve dynamic target detection and automatic monitor, which the change of target location and quantity can be detected by deep learning and recorded in blockchain. Meanwhile, if risk is detected, it will automatically trigger the alarm smart contract to realize automatic monitoring. We further implement a software prototype on fabric framework with real-world scenarios. Experiment results from real-world images show that the feasibility, usability and scalability of our proposed dynamic target detection and automatic monitor scheme for mortgage loan.