Blockchain is a prominent and leading decentralized ledger technology that has gained global attention and adoption across various industries. Long-range attacks (LRAs) are when an adversary attempts to rewrite the blockchain’s history from a point far back in time. Since PoS Blockchain relies on validators’ stakes as a form of security, LRAs can potentially undermine the network’s security if not detected and prevented. In order to protect against long-range attacks, this research suggests a high-performance explainable neural network model that can accurately categorize nodes as malicious or non-malicious while maintaining interpretability. The proposed explainable neural network model includes Residual Auto Encoder (RAE) guided generalized additive models with incorporating structured interactions (RAE-GAMI-Net) for LRA detection in PoS Blockchain In this work, a wrapper-based Binary Orchard Algorithm (W-BOA) is used to find the best features to lessen the dimensionality of extracted Characteristics, and a global feature extraction has been implemented based on multi-scale Densenet (MDensenet) that assures early convergence and optimal performance by providing global optimal solution. Then, the transformed features are used to train the RAE-GAMI-Net-based model to detect the LR attack. The included RAE learns a compressed representation (latent) of the input features. Then, the latent features are classified with GAMI-Net, balancing the model interpretability and accuracy. The effectiveness of our proposed method is assessed using the Proof of Stake blockchain dataset and benchmarked against other deep learning techniques. Our approach yields significant enhancements in accuracy (0.962), precision (0.9614), and recall 0.9604, accompanied by a notably low Brier score of 0.038.
Mr. Amar More, Mr . Karan More, Mr . Nikhil Neavse, Mr. Prasanna Deokar · 6 authors
This project introduces a decentralized file storage system that leverages blockchain technology to create a secure, immutable, and tamper-resistant platform for file sharing. By storing files within blocks on a blockchain, the system ensures that once data is uploaded, it cannot be altered or deleted, making it ideal for applications where data integrity is critical. Users interact with the platform through a web interface, allowing them to upload, download, and share files across a peer-to-peer network. The blockchain structure used in this project employs a Proof of Work (PoW) consensus mechanism, requiring peers (miners) to solve cryptographic puzzles to validate blocks and add them to the chain. Two different PoW methods are used: one generates nonces at random, while the other increases the nonce value one after the other. By comparing the effectiveness and security of different methods, the project finds that random nonce generation outperforms them at higher difficulty levels, providing quicker block validation and more robust defense against possible assaults. On the other hand, the incremental approach is less secure over time because it is simpler to foresee. The project also covers the advantages of on-chain storage, which involves storing files directly inside blockchain blocks. This approach offers better security but comes at the expense of more processing power. Furthermore, it investigates alternatives such as off-chain blockchain architectures for more effective file storage in subsequent iterations and Proof of Stake (PoS) for lowering resource use
Recent years have seen extensive adoption of blockchain technology across a variety of application domains, all with the goal of enhancing data privacy, system trustworthiness, and security. One of the biggest problems with blockchain is its inability to scale; other problems include energy consumption, latency, throughput, and the ever-increasing volume of daily transactions. The consensus technique relies on hash functions, which are important to highlight. Thus, such development is fundamental to blockchain advances in terms of structure. This study introduces a revolutionary change to the Proof-of-Stake (POS) consensus methods by suggesting the replacement of the commonly used SHA256 hash function with the extremely efficient Blake3. Many blockchain-based systems, including POS algorithms, still employ the widely used SHA256 algorithm for cryptographic hashing. Nevertheless, fresh research has shown that SHA256 has performance and security flaws. We show that the Blake3 hash function, is better than the SHA256 hash in many respects, including latency, throughput, and energy, via rigorous testing and functional analysis. Diverse parameters were utilized, including the quantity of blocks and validators. Seen cases are taken into account for performance evaluation. In the initial scenario, utilizing 500 blocks and 4 validators, our proposed methodology has surpassed the benchmark by achieving a 66% reduction in latency, over 50% in throughput, and a 55% decrease in energy consumption. The rate of enhancement is nearly uniform across all other instances, indicating that the implementation of Blake3 within the conventional POS consensus mechanism has demonstrated its advantages.
The development of artificial intelligence (AI) based medical Internet of Things (IoT) technology plays a crucial role in making the collection and exchange of medical information more convenient. However, security, privacy, and efficiency issues during information exchange have become pressing challenges. While many scholars have proposed solutions based on AI and blockchain to address these issues, few have focused on the impact of the slow consensus algorithm of blockchain on the efficiency of information exchange. To improve the efficiency of information exchange, we propose an information exchange approach based on AI and DAG-enabled blockchain, providing a secure and efficient environment for information exchange in the medical IoT. Additionally, to enhance the efficiency of information exchange in the medical IoT, a novel tip selection algorithm is introduced to reduce the time delay in reaching consensus, thereby enabling faster acquisition of trusted information via blockchain. Simulation results demonstrate that compared to methods based on traditional DAG-enabled blockchain, the approach proposed in this paper improves the efficiency of information exchange.
Machine learning models can support decision-making in mobile terminals (MTs) deployments, but their training generally requires massive datasets and abundant computation resources. This is challenging in practice due to the resource constraints of many MTs. To address this issue, data-parallel distributed learning can be conducted by offloading computation tasks from MTs to the edge-layer nodes. To facilitate the establishment of trust, one can leverage trust management, say to use trust values derived from local model quality and evaluations by other nodes as access criteria. Nonetheless, security and performance considerations remain unsolved. In this paper, we propose a blockchain-assisted dynamic trust management scheme for distributed learning, which comprises nodes attributes registration, trust calculation, information saving, and block writing. The proof of stake (PoS) consensus mechanism is leveraged to enable efficient consensus among the nodes using trust values as stakes. The incentive mechanism and corresponding dynamic optimization are then proposed to further improve system performance and security. The reinforcement-learning approach is leveraged to provide the optimal strategy for nodes’ local iterations and selection. Simulations and security analysis demonstrate that our proposed scheme can achieve an optimal trade-off between efficiency and quality of distributed learning while maintaining system security.
The use of blockchain technology has disrupted many industries, including finance, healthcare, and logistics. However, the use cases for blockchain technology are not limited to these industries. This paper presents a pet tutorial dApp built using Solidity coding language on the Ethereum blockchain. The dApp aims to provide a solution for pet owners to track their pets' medical records, schedule appointments with veterinarians, and even find nearby pet-friendly locations. The growing development and widespread adoption of blockchain technology have increased interest in blockchain-based decentralized apps (dApp). Numerous resources are used to crowdfund different kinds of dApp. dApps' definitions and typical architectures are presented first. Finally, we give a summary of the recent research issues with DApps from several angles. In recent trends dApp has become very popular. Decentralized applications (dApps) built on blockchain offer several benefits over traditional centralized systems, including improved security, transparency, and reduced transaction costs. This paper presents a pet tutorial dApp built using Solidity coding language on the Ethereum blockchain. There is an possibility of tracking the medical records in this dApp schedule appointments with veterinarians, and even find nearby pet-friendly locations. This research paper aims to provide a step-by-step guide to building a simple dApp using Solidity, and demonstrates how blockchain technology can be used to build useful and practical applications.
Cloud computing, the fastest-growing IT technology, offers flexibility and scalability with pay-per-use models, but it raises concerns about data security due to third-party storage and online accessibility. Effective cloud security measures are crucial for protecting user data. The Internet of Things (IoT) generates enormous amounts of data that must be efficiently processed and stored in the cloud. Blockchain technology enhances data security through its tamper-resistant distributed ledger and peer-to-peer networks, which allow any node with internet access to join or create networks. To protect sensitive data and maintain its integrity, it is crucial to implement robust encryption, privacy safeguards, access controls, identity management and confidentiality in both cloud and decentralized systems.
Bitcoin, as the most widely recognized cryptocurrency, has attracted significant global attention from businesses, consumers, and investors. This study introduces a hybrid model that integrates an online self-learning stochastic configuration network (OSL-SCN) with conformal prediction. The model autonomously adjusts its parameters in response to real-time data.Predictions from the OSL-SCN are refined through conformal prediction, which generates confidence intervals to enhance reliability. The results, using historical Bitcoin prices from Wikipedia, demonstrate that the combined approach enhances both prediction accuracy and reliability.
Managing and securing patient data in the healthcare sector is critical. While existing research on integrating blockchain technology with healthcare systems shows promise in enhancing data security and privacy, challenges in data analysis and decision-making accuracy persist. Traditional methods often struggle with imprecise and incomplete data, resulting in unreliable outcomes. This paper proposes a novel framework that integrates blockchain pervasive ledger technology with rough set theory to address these issues. By leveraging the immutable and transparent nature of blockchain and the data analysis capabilities of rough set theory, we aim to improve data security, privacy, and decision-making in healthcare. The proposed framework employs the Apriori algorithm for rule extraction and the Proof of Stake (PoS) consensus mechanism for blockchain transactions. We demonstrate the framework's effectiveness using a real-world dataset from the MIMIC-III (Medical Information Mart for Intensive Care III) database, which includes detailed patient records such as symptoms, test results, diagnoses, and treatment outcomes. The system architecture and mathematical foundations are discussed, along with empirical results showcasing enhanced decision-making accuracy and data security. Our results indicate a significant improvement in data analysis and security, with a 92% accuracy rate in medical diagnoses compared to traditional systems. This research contributes to the development of more reliable and secure healthcare data management systems, ultimately improving patient care and outcomes.
To achieve fine-grained access control and address the data silos challenge in data sharing, the integration of blockchain with attribute-based encryption emerges as a promising solution. Nowadays, the growing interconnectedness among diverse blockchain applications has spurred the need for efficient cross-chain data sharing. However, existing single-authority attribute-based data sharing schemes are not suitable for such cross-chain scenarios involving multiple attribute authorities. Moreover, the frequent requirement for data owners to process cross-chain data requests significantly hampers practicality. In this context, we introduce a novel multi-authority attribute-based proxy re-encryption scheme that enables ciphertext policy updating and supports secure and efficient cross-chain data sharing. By introducing a proxy, the data owner is empowered to delegate access without leaking any valid information and flexibly sells data across blockchains through cross-chain access policies. Besides, our scheme leverages the relay chain to foster a decentralized and trustworthy ecosystem. The adoption of smart contracts automates the cross-chain data sharing process and ensures equitable distribution of benefits among participants. Additionally, our scheme integrates hybrid encryption with the decentralized data hosting platform, substantially mitigating the on-chain storage burden. Security analysis affirms that our scheme is semantically secure and resistant to collusion attack. Performance analysis and simulation experiments demonstrate the excellent efficiency and practicality of our scheme when conducting cross-chain data sharing.
Abdullah Ayub Khan, Asif Ali Laghari, Abdullah M. Baqasah, Rex Bacarra · 7 authors
The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices.
With more and more data being generated, identifying a secure and effective data access framework has become a crucial research issue. Technological evolutions have been made in numerous areas to name a few being, agriculture, industry and specifically healthcare systems. Healthcare has experienced several remarkable alternates as the comprehensive transformation from document-based storage to electronic health records (EHR). The centralized mechanisms utilized by medical institutions for the Electronic Medical Records (EMR) management and transfer can be highly susceptible to security and privacy menaces. Blockchain have been a fascinating research area over a long period of long time and the advantages it imparts have been utilized by a number of several industries. In a similar manner, the healthcare sector stands to ease extensively from the blockchain technology owing to security and privacy. In this work to securitize patient record and access using a method called Ethereum Smart Contract Graph Embedded and Pyramid Network (ESCGE-PN) is proposed. In this paper, we first design a Graph Embedding-based Neural Network and incorporate it into Differentiable Permutation Invariant Ethereum Smart Contract to securitize patient medical record. Our proposed Differentiable Permutation Invariant Ethereum Smart Contract Graph Embedding-based Neural Network makes managing healthcare records using Permutation Invariant operator to accept arbitrary samples and maps patient records with the corresponding face images to form blocks in blockchain. We then propose novel Feature Fusion Pyramid Network Cosine Similarity-based face recognition for patient information access, which uses blockchain along with the the Feature Fusion Pyramid Network to address security concerns and enables selective sharing of medical records between doctors and patients. To measure the ESCGE-PN methods performance, four different performance metrics, data confidentiality, data integrity, recognition accuracy and recognition error are validated and analyzed. As a result, ESCGE-PN method achieved a higher performance compared to other state-of-the-art methods.
Yanming Zhu, Xuefei Yin, Alan Wee‐Chung Liew, Hui Tian
With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis.
The advent of sixth-generation (6G) technology is poised to revolutionize connectivity, particularly by enhancing the integration of Internet of Medical Things (IoMT) devices. This advancement offers ultra-fast data transmission, low latency, and high mobility but also brings the challenge of ensuring secure and energy-efficient communication. To solve these challenges, this paper introduces a novel hybrid greylag goose-based optimized clustering (HGGOC) algorithm. It merges the efficiency of greylag goose optimization with the precision of the golden sine strategy. The Lévy flight mechanism guides the algorithm to optimize cluster head selection in 6G-enabled IoMT networks. The integration of blockchain technology further strengthens data security and transparency. Simulation results show that HGGOC surpasses existing methods, delivering up to 64% improvement in network stability, a 47% increase in node lifetime, and a 59% boost in energy efficiency and data throughput. These findings position HGGOC as a promising solution for sustainable communication in 6G-enabled IoMT environments.
Willian Macedo Rodrigues, Silvia das Dores Rissino, Karin Satie Komati
Blockchain technology is a distributed ledger designed to record all transactions within its network, characterized by its decentralized nature, resistance to tampering, and attributes such as consistency, anonymity, and traceability. However, evaluating blockchain applications' performance can be complex due to their intricate and distributed infrastructure. This research employs machine learning model-based methods to predict blockchain systems' performance using predetermined configuration parameters. The data used in this study is derived from a blockchain simulator, generating blockchain data to facilitate performance predictions. The simulation process involves using simulated data and configuration settings for each run, including parameters such as the number of nodes, the number of miners, consensus algorithm, maximum block size, and transaction quantities, among others. Output metrics such as the total number of blocks, transaction rate, block propagation time, and latency are utilized to assess network performance. The simulator was run 184 times with various configurations. Our findings indicate that the Random Forest model outperformed other models used in the experiments, achieving the highest R² scores for multiple metrics, such as 0.987 for total number of transactions and 0.765 for average block propagation time, while also demonstrating lower RMSE values, indicating more accurate predictions.
Contributing significantly to the accomplishment of renewable energy goals and the overall decrease in carbon emissions, this is crucial in verifying and recording the production of renewable energies. However, a few problems with the current REC market have to do with effectiveness, accessibility, and transparency. An in-depth evaluation of the REC tokenisation concept is presented in this article, with an emphasis on the Ethereum blockchain platform. This article examines the current system's flaws and difficulties through the lens of blockchain technology's potential usage in renewable energy certificate man- agement. Its goal is to provide a framework for comprehensive process control, tracking, and accounting of renewable energy related to transactions. This checks that the distribution is secure and leaves a trail of ownership from the donor to the receivers. It fixes the issue where the power trading centre platform doesn't have the tools to track and evaluate its renewable energy usage.
Blockchain systems have seen extensive research and adoption. However, its standardization remains challenging, hindering seamless interoperability and value transfer across diverse blockchain ecosystems. In addition, merging or conversion of heterogeneous chains in large-scale systems is impossible due to extensive variations in the fields and structures of the blocks. To address this challenge, this paper proposes a solution based on a unified and standardized representation of different BC ledgers. The proposed approach employs Type-Length-Value (TLV) encoding to facilitate a unified block structure. This mechanism allows the translation or conversion of existing blocks, merging heterogeneous chains, and creating hybrid chains with diverse block and transaction structures. Hence, facilitating large-scale interoperability among existing and new blockchain systems. Prototype implementation shows that the proposed mechanism is self-sustaining and has negligible processing and storage impact on distributed ledgers.
Jie Yang, Yuta Kodera, Samsul Huda, Yasuyuki Nogami
In the distributed medical information management system, blockchain technology has more obvious advantages in terms of data tampering data traceability. However, considering the scalability issues of current Layer-1 blockchain, uploading massive medical information data onto the blockchain will add non-negligible space storage pressure. Patients are also unable to have a comprehensive grasp of their personal privacy information retained in these data, which can lead to concern about the system’s privacy and security. To cope with these challenges, this paper introduced Layer-2 network to reduce the data space occupation in a single block. By storing unnecessary information in the off-chain channel, the transaction speed and throughput of the system would be improved. In addition, to strengthen the security within the process of data communication and storage, this paper also deployed the system based on consortium blockchain utilizing AWS cloud service. And the system integrated with the zero-knowledge proof algorithm zk-SNARKs to protect personal information privacy. According to the experimental simulation and analysis, the proposed scheme can reduce data storage space in the Lay-1 blockchain. What’s more, it also allows quick verification of on-chain data without disclosing personal privacy information.
A new quantitative model to estimate the performance of a PoS (Proof of Stake) consensus protocol-based blockchain (e.g., Ethereum) is proposed in this paper. The proposed new PoS-based chain model has the number of validators (m) in the network as a central variable in the model such that m = 1 in PoW and m ≫ 1 in PoS. A unified binomial distribution is assumed with respect to the number of validators and then a multinomial distribution is assumed with respect to the number of transaction slots pending on the current block to be posted [14], thereby establishing a quantitative model to express the steady-state probability to have 0 ≤ i ≤ n number of trasactions slots with 1 ≤ j ≤ number of validators across the blockchain network given transaction slots arrival rate (λ) and block posting rate $\left( {\frac{\mu }{j}} \right)$ by number of validators. Extensive numerical simulations will be conducted to evaluate a few base performance metrics such as average transaction waiting time, average size of block and throughput, to mention a few. The simulation results will reveal a quantitative insight into the PoS in comparison to PoW. Ultimately, the proposed quantitative model will establish a theoretical foundation to guide the PoS-based chain developers with specific respect to performance.
Bilal A. Salih Ozturk, Huda Kadhim Tayyeh, Heba Emad Namiq, Hemant B. Mahajan · 10 authors
ABSTRACT Cloud‐based Electronic Health Records (EHRs) have seen a substantial increase in usage in recent years, especially for remote patient monitoring. Researchers are interested in investigating the use of Healthcare 4.0 in smart cities. This involves using Internet of Things (IoT) devices and cloud computing to remotely access medical processes. Healthcare 4.0 focuses on the systematic gathering, merging, transmission, sharing, and retention of medical information at regular intervals. Protecting the confidential and private information of patients presents several challenges in terms of thwarting illegal intrusion by hackers. Therefore, it is essential to prioritize the protection of patient medical data that is stored, accessed, and shared on the cloud to avoid unauthorized access or compromise by the authorized components of E‐healthcare systems. A multitude of cryptographic methodologies have been devised to offer safe storage, exchange, and access to medical data in cloud service provider (CSP) environments. Traditional methods have not been effective in providing a harmonious integration of the essential components for EHR security solutions, such as efficient computing, verification on the service side, verification on the user side, independence from a trusted third party, and strong security. Recently, there has been a lot of interest in security solutions that are based on blockchain technology. These solutions are highly effective in safeguarding data storage and exchange while using little computational resources. The researchers focused their efforts exclusively on blockchain technology, namely on Bitcoin. The present emphasis has been on the secure management of healthcare records through the utilization of blockchain technology. This study offers a thorough examination of modern blockchain‐based methods for protecting medical data, regardless of whether cloud computing is utilized or not. This study utilizes and evaluates several strategies that make use of blockchain. The study presents a comprehensive analysis of research gaps, issues, and a future roadmap that contributes to the progress of new Healthcare 4.0 technologies, as demonstrated by research investigations.
Maryam Fattahi Vanani, Hamidreza Shayegh Borujeni, Ali Nourollah
Blockchain has made strides in multiple fields, including education. It offers transparent, secure, and decentralized storage for student records. A transparent assessment process, provided by blockchain is also vital in education. However, as the number of users and transactions grows, storage costs rise, and network performance declines. Therefore, a method is needed to increase scalability in a blockchain-based learning network, while keeping data and transactions secure. This paper presents a secure learning system using blockchain which improves the storage process, enables secure data sharing, and ensures assessment transparency through smart contracts. It utilizes a private Ethereum network and off-chain storage to enhance network performance and reduce costs. The proposed system aims to improve network scalability while maintaining security and data access control using encryption. The experimental results show that the proposed system has recorded a significant improvement in average transaction latency and has reduced file storage costs.
In this work, a patient-centric paradigm utilizing IPFS (InterPlanetary File System) storage, blockchain technology, and Zero-Knowledge Proofs (ZKPs) is proposed for handling healthcare data. Traditional healthcare data management systems frequently encounter interoperability, data security, and privacy issues. The technology guarantees safe, decentralized storage and convenient access to medical records by integrating blockchain with IPFS, while ZKPs offer strong privacy protection by permitting key verification without disclosing sensitive information. A framework is proposed that allows healthcare organizations to manage decentralized, tamper-proof healthcare ledgers. Hospitals and doctors are lightweight nodes, although patient nodes may be full or lightweight nodes. The suggested model aims to manage health data through off-chain storage. The technique, which is built on IPFS, protects the blockchain architecture from problems related to scalability. ZKPs also assist in recovering the patient's keys in case they are misplaced or forgotten. With the least amount of effort, the strategy attempts to address and mitigate the patient-centric model's deficits. This method improves efficiency and confidence in healthcare data management while simultaneously giving people ownership over their data. Additionally, the healthcare system is made more resilient, scalable, and safe while protecting patient privacy by integrating blockchain technology with IPFS.
The proliferation of online multimedia content demands effective approaches for semantic exchange of videos and data authentication for information transfer. Video summaries offer an efficient way to enhance user experience by providing a concise, meaningful overview of video content, reducing bandwidth strain while maintaining relevance. The emergence of Web 3.0, an evolution that incorporates blockchain, edge computing, semantic communication, and artificial intelligence, has revolutionized decentralized information sharing, aiming for a user-centric, interconnected, and trustless Internet. However, current Web 3.0 approaches often underutilize cutting-edge technologies that extend beyond blockchain. This article presents a pioneering blockchain-based semantic exchange framework for video summaries within a wireless edge intelligence network, with a special focus on keyframe extraction for secure and reliable transmission. By leveraging blockchain through Non-Fungible Tokens for integrity verification, this framework ensures tamper-proof access to critical video summaries on user devices.
Leveraging blockchain in Federated Learning (FL) emerges as a new paradigm for secure collaborative learning on Massive Edge Networks (MENs). As the scale of MENs increases, it becomes more difficult to implement and manage a blockchain among edge devices due to complex communication topologies, heterogeneous computation capabilities, and limited storage capacities. Moreover, the lack of a standard metric for blockchain security becomes a significant issue. To address these challenges, we propose a lightweight blockchain for verifiable and scalable FL, namely LiteChain, to provide efficient and secure services in MENs. Specifically, we develop a distributed clustering algorithm to reorganize MENs into a two-level structure to improve communication and computing efficiency under security requirements. Moreover, we introduce a Comprehensive Byzantine Fault Tolerance (CBFT) consensus mechanism and a secure update mechanism to ensure the security of model transactions through LiteChain. Our experiments based on Hyperledger Fabric demonstrate that LiteChain presents the lowest end-to-end latency and on-chain storage overheads across various network scales, outperforming the other two benchmarks. In addition, LiteChain exhibits a high level of robustness against replay and data poisoning attacks.