Federated Learning (FL) faces significant challenges arising from both data and system heterogeneity. While Clustered Federated Learning (CFL) mitigates data heterogeneity by grouping clients with similar data distributions, it remains vulnerable to system heterogeneity, which can slow convergence due to performance disparities among clients. Moreover, data drift may degrade clustering accuracy and training efficiency over time. In this work, we propose a Model Structure-aware Clustered Federated Learning (MSCFL) framework that simultaneously addresses the issues of data heterogeneity, system heterogeneity, and data drift. MSCFL incorporates model pruning (MP) into the CFL framework to enhance training efficiency under system heterogeneity. To enable this integration, we address the key challenge of performing effective clustering based on heterogeneous, pruned local models with varying structures. To this end, we design a model structure-based similarity computation algorithm to integrate CFL with MP. To effectively address data drift, we propose a dynamic cluster migration strategy that efficiently monitors model structures via Hamming Distance and triggers re-clustering only when necessary. Extensive experimental results show that MSCFL improves the accuracy and convergence speed of cluster models, outperforming traditional CFL in various settings.

In current Data-as-a-Service (DaaS) platforms, data marketplaces are the primary mechanism by which data owners deliver data products and services to users. However, data owners incur substantial costs to collect or produce data before selling it, exposing them to economic risks due to market price fluctuations. Options trading, used in traditional commodity markets, can alleviate sellers' financial burdens with forward contracts at fixed option prices and pre-paid option premiums. To our knowledge, the application of option trading in data marketplaces has not been thoroughly studied or implemented. In this work, we advocate for the first data options marketplace on a DaaS platform that mitigates price fluctuation risks and reduces market entry barriers. Designing such a marketplace involves several key challenges, including the easy replication of data and the difficulty in assessing data quality before production. To address these challenges, we first design a data quality prediction method based on sellers' reputation. Following this, we model and quantify the competitive relationships among buyers. On this basis, we model the interactions between sellers and buyers in the data options market as a two-stage Stackelberg game, focusing on maximizing sellers' profit. We formally derive the perfect subgame equilibrium for option trading and derive each seller's optimal pricing strategy. Numerical experiments demonstrate the superiority of data options trading over conventional data trading methods in DaaS platforms.

Training deep neural networks is resource-intensive, making it crucial to protect their intellectual property from infringement. However, current model ownership resolution (MOR) methods predominantly address general removal attacks that involve weight modifications, with limited research considering alternative attack perspectives. In this work, we propose a frequency-based model ownership removal attack, grounded in a key observation: modifying a model's high-frequency coefficients does not significantly impact its performance but does alter its weights and decision boundary. This change invalidates the existing MOR methods. We further propose a frequency-based fingerprinting technique as a defense mechanism. By extracting frequency-domain characteristics instead of decision boundary or model weights, our fingerprinting defense effectively against the proposed frequency-based removal attack and demonstrates robustness against existing general removal attacks. The experimental results show that the frequency-based removal attack can easily defeat state-of-the-art white-box watermarking and fingerprinting schemes while preserving model performance, and the proposed defense method is also effective. Our code is released at: https://github.com/huangtingqiao/RRA-IJCAI25.

Although the P2P power transactions using the multiagent deep deterministic policy gradient (MADDPG) algorithm has been extensively studied, there are still challenges in privacy protection and training incentives. Furthermore, the stability and efficiency of the strategy decreases when dealing with nonindependent identically distribution (Non-IID) data from heterogeneous prosumers. Therefore, this article proposes a blockchain-enabled asynchronous federated learning-MADDPG (BEAFL-MADDPG) framework designed to enhance the training efficiency of heterogeneous prosumers while safeguarding data privacy. The framework includes a novel P2P energy trading model that facilitates energy trading amidst incomplete information while ensuring privacy assurances. In addition, a BEAFL-MADDPG algorithm is proposed, which accelerates training processes and enables parallel computation among agents. This algorithm enhances the efficiency of algorithm and empowers the training of diverse prosumers. Furthermore, a blockchain-enabled training mechanism and prosumer incentive scheme are proposed that not only encourage prosumer engagement in training but also ensure traceable transactions without the need for trust among participants. These mechanisms promote transparency and integrity, fostering a collaborative and secure environment for energy trading. Simulation results demonstrate that the framework achieves peak load reduction through optimized P2P trading, maintains computation efficiency across discount rates, and ensures secure transactions via blockchain-based incentives. These practical benefits support scalable and sustainable community microgrid operations.

As mobile devices proliferate and mobile applications diversify, Mobile Edge Computing (MEC) has become widely adopted to efficiently allocate computing resources at the network edge and alleviate network congestion. In the MEC initial phase, the absence of vital information presents challenges in devising task-offloading policies, and identifying malicious devices responsible for providing inaccurate feedback is complex. To fill in such gaps, we introduce a consortium blockchain-enabledCommitteeVoting basedTaskOffloadingModel (CVTOM) to collaboratively formulate resource allocation policies and establish deterrence against malicious servers producing erroneous results intentionally. Different voting principle mechanisms of each committee member are first designed in a Blockchain-enabled system which helps to represent the system's resource status. Additionally, we propose a Multi-armed Bandits relatedThompsonSampling basedAdaptivePreferenceOptimization (TSAPO) algorithm for task-offloading policy, enhancing the timely identification of potent edge servers to improve computing resource utilization which first considers dynamic edge server space and parallel computing scenarios. The solid proof process greatly contributes to the theoretical analysis of the TSAPO. The simulation experiments demonstrate the delay and budget can be reduced by around 25% and 10% respectively, showcasing the superior performance of our approach.

Clustered federated learning (CFL) leverages data distribution similarities to cluster clients, facilitating personalized model training under data heterogeneity. However, most existing CFL schemes pose potential privacy risks for clients (e.g., gradient inversion attacks) as they rely on individual gradients for clustering. This also renders them incompatible with secure aggregation mechanisms that are widely employed in federated learning for privacy protection. Moreover, CFL introduces the risk of malicious clients dominating several clusters and conducting poisoning attacks therein, thereby threatening secure model training. To address these issues, we propose ProCFL, a Privacy-Enhanced and Robust CFL framework incorporating gradient-free clustering and peer validation. Specifically, we first design a new protocol for measuring data distribution similarity among clients without using their gradient information. Then, we transform the client clustering process into a weighted set covering problem and introduce a diversity-optimized clustering algorithm to achieve near-optimal clustering results while eliminating any need for prior knowledge. Furthermore, we develop a post-hoc detection mechanism that employs peer validation to identify and discard malicious client models. Extensive experimental evaluation of ProCFL validates its superior model robustness and accuracy performance compared to existing schemes.

Decentralized federated learning (DFL) overcomes the single point of failure issue of centralized federated learning. Building upon DFL, blockchain-based federated learning (BFL) takes further strides in establishing trust, enhancing security, and fault tolerance. However, BFL based on the classical linear blockchain exhibits diminished training efficiency in heterogeneous environments and is limited by the performance bottleneck of blockchain. Recent solutions introduce the directed acyclic graph (DAG) blockchain to address these issues, yet they compromise the verifiability of BFL, struggle with handling outdated models, and have a slow convergence speed. In this paper, we propose TGFL, a decentralized federated learning framework based on the Tree-Graph blockchain. The underlying blockchain structure of TGFL is designed as a block-centered DAG to support verifiable and semi-asynchronous training. To facilitate fast convergence, we design a pivot chain generation algorithm that topologically sorts the semi-asynchronous training process, guiding participants in sampling appropriate models. The consensus mechanism, which is closely integrated with federated learning, ensures that the TGFL can effectively resist attacks on the model and the blockchain system. Extensive experiments in various settings demonstrate that TGFL can achieve better training efficiency and model accuracy compared to three baselines.

Blockchain provides trustworthy properties such as decentralization, traceability, and transparency, and has been applied in various fields. Given the complexity of guaranteeing the accuracy of the input data, erroneous data may be stored on the blockchain, making it hard to modify. As an effective solution, redactable blockchain allows on-chain data to be modified by introducing a chameleon hash function with a trapdoor, which enables on-chain erroneous data to be corrected. However, existing redactable blockchain solutions often require trusted third parties, have huge overhead, or lack effective accountability mechanisms to meet data security needs. Therefore, this paper proposes a secure, efficient and accountable data management scheme based on redactable blockchain. To cope with the possible frequent editing needs, we design an efficient and accountable distributed trapdoor recovery mechanism that reliably maintains data security while avoiding the security risks associated with centralized management. Various information during the trapdoor management process is also recorded on the chain as the basis for implementing accountability mechanisms. In addition, the one-time trapdoor technology allows the blockchain system to reduce the maintenance complexity of the system by eliminating the need to frequently update the system parameters when partial trapdoor information needs to be published. Theoretical and experimental results show that our scheme can achieve an efficient accountability mechanism while modifying the data on the chain at low cost.

Model migration can accelerate model convergence during federated learning on the Internet of Things (IoT) devices and reduce training costs by transferring feature extractors from fast to slow devices, which, in turn, enables sustainable computing. However, malicious or lazy devices may migrate the fake models or resist sharing models for their benefit, reducing the desired efficiency and reliability of a federated learning system. To this end, this work presents a blockchain-based model migration approach for resource-constrained IoT systems. The proposed approach aims to achieve secure model migration and speed up model training while minimizing computation cost. We first develop an incentive mechanism considering the economic benefits of fast devices, which breaks the Nash equilibrium established by lazy devices and encourages capable devices to train and share models. Second, we design a clustering-based algorithm for identifying malicious devices and preventing them from defrauding incentives. Third, we use blockchain to ensure trustworthiness in model migration and incentive processes. Blockchain records the interaction between the central server and IoT devices and runs the incentive algorithm without exposing the devices’ private data. Theoretical analysis and experimental results show that the proposed approach can accelerate federated learning rates, reduce model training computation costs to increase sustainability, and resist malicious attacks.

The Internet of vehicles (IoV) has a substantial impact on traffic efficiency improvement and accidents avoidance. Due to restricted resources, vehicles must share observed data with RSUs and other vehicles to execute some time-tolerant computing tasks. However, data provided by vehicles cannot always be trusted due to the presence of attackers. Fake messages could have catastrophic ramifications, such as vehicle collisions. Furthermore, extensive data sharing might cause channel congestion, resulting in the loss of vital messages during delivery. To overcome the aforementioned issues, we propose TRUCON, a blockchain-based trusted data sharing mechanism with congestion control in IoV. Firstly, we propose a Kademlia algorithm-based traffic data forwarding method to control channel congestion state. By adjusting the bucket size and distance threshold, source vehicles can limit the number of reference vehicles forwarded. Secondly, we present a cuckoo filter-based traffic data deduplication and discrimination approach. To avoid repetitive sharing, vehicles and RSUs can check their local filters to verify if the current data report has been shared. Based on the foregoing, we propose a blockchain-based trust management mechanism with congestion control. RSUs serve as full nodes while vehicles are light nodes in the blockchain. Finally, we develop a trust management prototype system with congestion control that incorporates both on-chain and off-chain parts. It signifies that our scheme is both feasible and effective.

Renewable energy sources (RES) and low-carbon technology users play a vital role in modern power systems. However, RES generation is easily affected by the environment. Meanwhile, the load, such as electric vehicles (EVs) and prosumers, accounts for most low-carbon technology users. Their power is usually superimposed on peak loads without dispatching, which also exacerbates the instability of the power system. Current optimal dispatching mechanisms mainly rely on centralized organizations, while their dispatching process is not open and transparent. In this article, we propose a blockchain-based trustworthy dispatching approach for the distribution network in high renewable energy penetrated power systems. We first develop an optimal dispatching model considering EVs’ charging behavior and the prosumers’ economic benefits. With the model, prosumers can be dispatched to balance power and consume renewable energy, reducing the impact of disorderly charging on the grid and the abandonment of RES generation. An orderly charging iteration optimization (OCIO) algorithm is proposed to implement orderly EV charging while considering the charging cost and the period. We also propose a modified particle swarm optimization (mPSO) algorithm to publish dispatching tasks based on real-time power balance. Furthermore, blockchain is applied as an open and transparent ledger to record each entity’s power generation and consumption information, ensuring that the dispatching process is trustworthy. Finally, the effectiveness of the dispatching approach is verified in the modified IEEE 33-bus test system and Ethereum-based smart contracts.

Network storage services have benefited countless users worldwide due to the notable features of convenience, economy and high availability. Since a single service provider is not always reliable enough, more complex multi-cloud storage systems are developed for mitigating the data corruption risk. While a data auditing scheme is still needed in multi-cloud storage to help users confirm the integrity of their outsourced data. Unfortunately, most of the corresponding schemes rely on trusted institutions such as the centralized third-party auditor (TPA) and the cloud service organizer, and it is difficult to identify malicious service providers after service disputes. Therefore, we present a blockchain-based multi-cloud storage data auditing scheme to protect data integrity and accurately arbitrate service disputes. We not only introduce the blockchain to record the interactions among users, service providers, and organizers in data auditing process as evidence, but also employ the smart contract to detect service dispute, so as to enforce the untrusted organizer to honestly identify malicious service providers. We also use the blockchain network and homomorphic verifiable tags to achieve the low-cost batch verification without TPA. Theoretical analyses and experiments reveal that the scheme is effective in multi-cloud environments and the cost is acceptable.

Summary Owing to the rapid progress of network researching, attribute‐based access control (ABAC) has attracted more and more attention due to its appreciable expressiveness, flexibility, and scalability. Unfortunately, collecting user attributes is necessary to complete the standard ABAC decision process, which increases the risk of privacy disclosure. This problem increases public doubts about ABAC and hinders its popularization. In this paper, a privacy‐protected and efficient attribute‐based access control (EPABAC) scheme is proposed to prevent the privacy leakage of access subject in the decision‐making process of ABAC by introducing a novel hash‐based binary search tree. The analyses and experimental evaluations show that the EPABAC achieves user privacy protection in the decision‐making process with acceptable additional computing overhead.

The maturity of network storage technology drives users to outsource local data to remote servers. Since these servers are not reliable enough for keeping users' data, remote data auditing mechanisms are studied for mitigating the threat to data integrity. However, many traditional schemes achieve verifiable data integrity for users only without resolutions to data possession disputes, while others depend on centralized third-party auditors (TPAs) for credible arbitrations. Recently, the emergence of blockchain technology promotes inspiring countermeasures. In this article, we propose a decentralized arbitrable remote data auditing scheme for network storage service based on blockchain techniques. We use a smart contract to notarize integrity metadata of outsourced data recognized by users and servers on the blockchain, and also utilize the blockchain network as the self-recording channel for achieving non-repudiation verification interactions. We also propose a fairly arbitrable data auditing protocol with the support of the commutative hash technique, defending against dishonest provers and verifiers. Additionally, a decentralized adjudication mechanism is implemented by using the smart contract technique for creditably resolving data possession disputes without TPAs. The theoretical analysis and experimental evaluation reveal its effectiveness in undisputable data auditing and the limited requirement of costs.

Emerging network computing technologies extend the functionalities of industrial IoT (IIoT) terminals. However, this promising service-provisioning scheme encounters problems in untrusted and distributed IIoT scenarios because malicious service providers or clients may deny service provisions or usage for their own interests. Traditional nonrepudiation solutions fade in IIoT environments due to requirements of trusted third parties or unacceptable overheads. Fortunately, the blockchain revolution facilitates innovative solutions. In this paper, we propose a blockchain-based fair nonrepudiation service provisioning scheme for IIoT scenarios in which the blockchain is used as a service publisher and an evidence recorder. Each service is separately delivered via on-chain and off-chain channels with mandatory evidence submissions for nonrepudiation purpose. Moreover, a homomorphic-hash-based service verification method is designed that can function with mere on-chain evidence. And an impartial smart contract is implemented to resolve disputes. The security analysis demonstrates the dependability, and the evaluations reveal the effectiveness and efficiency.

The emerging network computing technologies have significantly extended the abilities of the resource‐constrained IoT devices through the network‐based service sharing techniques. However, such a flexible and scalable service provisioning paradigm brings increased security risks to terminals due to the untrustworthy exogenous service codes loading from the open network. Many existing security approaches are unsuitable for IoT environments due to the high difficulty of maintenance or the dependencies upon extra resources like specific hardware. Fortunately, the rise of blockchain technology has facilitated the development of service sharing methods and, at the same time, it appears a viable solution to numerous security problems. In this paper, we propose a novel blockchain‐based secure service provisioning mechanism for protecting lightweight clients from insecure services in network computing scenarios. We introduce the blockchain to maintain all the validity states of the off‐chain services and edge service providers for the IoT terminals to help them get rid of untrusted or discarded services through provider identification and service verification. In addition, we take advantage of smart contracts which can be triggered by the lightweight clients to help them check the validities of service providers and service codes according to the on‐chain transactions, thereby reducing the direct overhead on the IoT devices. Moreover, the adoptions of the consortium blockchain and the proof of authority consensus mechanism also help to achieve a high throughput. The theoretical security analysis and evaluation results show that our approach helps the lightweight clients get rid of untrusted edge service providers and insecure services effectively with acceptable latency and affordable costs.