The blockchain-based federated learning framework has garnered widespread attention to ensure data privacy and trustworthy computing in IoT devices. Improving the accuracy and scalability are paramount for the increasing demands of IoT tasks. However, existing solutions, such as SIFL and ChainFL, utilize traditional single-chain architecture and fixed models, which exhibit limitations in terms of model generalizability and scalability. To overcome the above problems, this paper proposesFedShard, an FL framework that integrates a layered dual-track incentive mechanism and a privacy knowledge distillation module.When implementingFedShard, we address two technical challenges: (1) to ensure efficient training among the numerous clients in the sharding architecture, we propose a layered dual-track incentive mechanism that provides both long- and short-term rewards; and (2) to enhance the framework’s convergence and privacy in sharding-based FL, we design a knowledge distillation module that incorporates local differential privacy. Furthermore, we propose a bucket-based hash ring to manage clients, enabling the framework to adapt to dynamic network environments. To validate the framework’s generalizability and scalability, we implementFedShardon a 48-core high-performance server using Fabric to conduct both on-chain and off-chain experiments. Our comprehensive experiments, comparingFedShardwith SIFL, PEFL, and ChainFL, reveal that our solution outperforms the state-of-the-art methods by achieving a notable 29% increase in accuracy and a 22% improvement in throughput.

Ensuring atomic execution of cross-shard transactions is a fundamental challenge for sharded blockchains, particularly in scenarios demand coordination across multiple shards. However, existing solutions either rely on on-chain coordination, leading to high communication overhead, or leverage secure hardware for off-chain execution, imposing strong trust assumptions and reducing general applicability. To this end, we propose RollShard, a sharded blockchain that integrates stateless off-chain mechanism to efficiently process multi-shard transactions (MSTs). In RollShard, each MST is abstracted into a transaction DAG by the Sequencer Shard to ensure the authenticity of the transaction content and the correctness of its execution order. Batched MSTs are dispatched to off-chain executors, each of which simulates transaction logic using a virtual zero-state model integrate with a hierarchical state-delta tree (HSDT). The HSDT employs a Merkle Sum tree to precisely capture batched MSTs’ impact on per-shard account states. Based on the HSDT, the executor generates the zero-knowledge proof to attest the correctness of each shard’s state changes and global value conservation. The resulting net state deltas are then optimistically committed to the relevant shards without cross-shard coordination, reducing intra-shard coordination. We design a game-theoretic incentive mechanism to ensure rational behavior of off-chain executors, showing that honest execution forms a Nash equilibrium under collateral staking. Experimental results based on a prototype deployed in a local area network demonstrate that ROLLSHARDsignificantly outperforms two baseline coordination models proposed in ByShard, namely the Linear and Distributed designs. Specifically, under high workload, RollShard improves throughput by 44.9% and 158%, and reduces cross-shard latency by 38.9% and 42.1%, compared to the Linear and Distributed models, respectively.

Blockchain-based Federated Learning (BCFL) has attracted considerable attention in the intelligent IoT domain for its privacy-preserving and decentralized characteristics. Depending on their applicable scenarios, BCFL frameworks are categorized into two types: synchronous and asynchronous. However, synchronous BCFL struggles with low efficiency in heterogeneous IoT environments, while asynchronous BCFL suffers from slow convergence speed. In additional, Both BCFL incur significant resource consumption from blockchain consensus mechanisms which is unrelated to federated learning tasks, leading to resource wastage and poor scalability, making them unsuitable for large-scale IoT networks. To address these challenges, we propose CoCFL, a novel BCFL framework utilizing multi-chain collaboration. CoCFL introduces two lightweight sub-chains: PoCFL-CChain and PC-CChain, based on different FL strategy. PoCFL-CChain uses a synchronous FL strategy for learning devices with similar performance to generate high-accuracy models, while PC-CChain adopts an asynchronous strategy for heterogeneous devices, which can improving training efficiency. CoCFL assigns devices to suitable sub-chains based on their performance to carry out FL tasks and aggregates the sub-chain models into a global model. This multi-chain collaboration strategy enhances model accuracy and convergence speed and significantly improves the scalability of BCFL. In additional, the consensus mechanisms in CoCFL sub-chains not only maintain the blockchain ledger but also handle FL-related tasks such as detecting poisoning attacks, assigning roles, and distributing incentives. This design not only improving the efficiency of BCFL, but also enhances learning security and ensuring fair incentives. Experiments show that CoCFL improves learning accuracy by 6% and efficiency by 18% over existing BCFL frameworks. It also demonstrates excellent scalability, with time consumption liner decreasing as sub-chains increase, and can withstand up to 40% of poisoning attacks while ensuring fair incentives.

Byzantine fault-tolerant (BFT) consensus protocols are the core components of blockchain. In the process of improving the performance of BFT protocols, existing work faces the following three problems: 1) the binary dilemma between the leader’s performance bottleneck in star-based linear communication and compromised resilience in tree-based sublinear communication; 2) two- or three-round protocols restrict the phase number of one proposal, thereby limiting the number of concurrent proposals and causing high latency. 3) The fixed timeout makes the protocol sensitive to varying network delays. Therefore, this paper proposesCrackle, the first sector-based pipelined BFT protocol with a sublinear communication complexity, for a throughput improvement of consensus protocol with max resilience of$(\mathcal {N}\textrm {-} 1)/3$. We propose a sector-based communication mode to disseminate messages from the leader to a subset of replicas in each phase to accelerate consensus and split the traditional two-round protocol into$2\mathsf {\kappa }$phases to increase the basic pipeline scale. We refine the timer strategy so that the timeout$\Delta $is adjusted with the proposal submission to cope with the changing network environment. We then address two technical challenges: 1) to ensure Quorum Certificate (QC) validation, we design a$\mathit {voteMap} s$field within each block, and verify QC by the signature aggregation of$\mathit {voteMap} s$in continuous$\mathsf {\kappa }$phases; and 2) to achieve pipeline decoupling among shorter phases, we propose a vote-appending mechanism that relaxes the conditions for the leader to send new proposals. We provide comprehensive theoretical proof of the correctness ofCrackle, including safety and$\mathit{liveness}$. Moreover, we implementCracklebased on a public BFT framework and deploy it on 64 cloud servers. Real experimental results reveal that ourCrackleprotocol achieves up to 10.36x higher throughput and can dynamically adapt to network delay compared with state-of-the-art BFT protocols such as Kauri and Hotstuff.

To enable parallel transaction processing in blockchain systems, recent consensus protocols have adopted directed acyclic graph (DAG) structures where DAG is used to organize and parallelize the blocks. Unfortunately, these protocols suffer from high communication overhead. Our experiment on the state-of-the-art Graded DAG[12] reveals that dissemination of transaction and consensus vote messages account for the majority of network traffic. We analyze that the overall overhead is O (N2) per replica and O (N3) for the entire system, where N is the number of replicas, and note that existing approaches have not succeeded in reducing this overhead.

Due to the exponential growth of blockchain ledger sizes, federated-storage which enables multiple devices to jointly store data, has emerged as a promising solution for secure data storage in edge computing. However, how to achieve verifiable queries in such decentralized storage remains underexplored. Existing broadcast-based query methods lack a verification mechanism for query results, making it impossible to ensure their correctness and completeness. Meanwhile, authenticated data structure based (ADS-based) query strategies are constrained by the full ledger data and cannot provide verifiable query services for users in a federated-storage environment. To this end, this paper takes the lead to propose EVQ, a verifiable blockchain keyword query scheme tailored for federated-storage edge computing. We propose a split keyword-based ADS as the core structure of our framework which ensures that users can verify the correctness and completeness of query results while alleviating storage pressure of edge devices. Specifically, the proposed ADS is constructed through a two-phase process: top-bottom keyword index tree construction and bottomtop RSA accumulator integration. Splitting the ADS based on keywords enables distributed data storage and the generation of corresponding ADS for the stored data. To reduce the query costs incurred by edge devices during query processing, we formulate the Keyword Allocation Optimization (KAO) problem and propose a gain-ratio-based keyword allocation mechanism to determine the splitting scheme of the ADS. The experiments are conducted based on the Foursquare dataset, which contains approximately 18 months of global check-in data collected from Foursquare. The experimental results show that, compared to the merkle tree strategy that also integrates the RSA accumulator, our EVQ improves query performance by 24.77 x.

Blockchain, with its immutability and decentralization, drives innovation in finance and supply chain, but the growing data volume makes storing complete ledger replicas impractical for users, especially in the resource-constrained Internet of Thing (IoT) scenarios. Existing solutions focus on nodes storing only a partial ledger to alleviate storage burdens. Nonetheless, these approaches prioritize storage optimization by minimizing the query cost and lack control over storage cost. Furthermore, these approaches overlook the relationships between network users, thus failing to fully measure the future query cost. Thus, this article proposes BSSN, a blockchain storage technology based on social networks. The combined use of storage cost and query cost is introduced for the first time to formulate the node allocation optimization (NAO) problem, and the multipopulation genetic ant colony (MGAC) algorithm will be employed to derive node allocation strategies. Specifically, we address three technical challenges: 1) to predict the transactions that nodes will participate in the future, we employ the social ties to obtain the access frequencies among users; 2) to strike a balance between the storage cost and query cost, we jointly model the two costs as a multiobjective optimization problem to formulate the NAO problem; and 3) to solve the NP-hard NAO problem, we use the MGAC algorithm, where the storage and query populations collaboratively search for solutions based on four operations. Extensive experiments indicate that compared with existing work, BSSN can reduce the average query cost to 67% with its adjustable storage cost, ensuring a balanced data storage among users.

Sharding has become a significant approach to enhance blockchain scalability. However, existing sharding techniques applied in IoT scenarios suffer from transaction congestion due to imbalanced distribution of transactions across shards, which hinders intra-shard transaction processing capacity. To overcome the above problems, this paper proposes HydraChain for IoT scenarios, the first multi-agent reinforcement learning based sharding blockchain system with account graph relationships, for a throughput improvement of shards under realtime load balancing. Agents collaborate by sharing information and jointly optimizing decisions, enhancing the accuracy and efficiency of the decision-making process. We first construct a sharding blockchain environment integrated with an embedded graph encoder. Concurrently, we propose a SG-MAPPO multiagent model with decoder, which enables agents to cooperatively learn to optimize account allocation strategies based on real-time shard load and global system information. When implementing HydraChain, we address two technical challenges: (i) to extract granular behavioral features from accounts with diverse and time-varying patterns, we design a graph data encoder, which constructs a graph network based on transactional relationship; and (ii) to ensure real-time load balancing under the constraints of dynamic transaction patterns, we propose a multi-agent model (SG-MAPPO), which matches graph encoding features within the environment. Our approach leverages the ability of multi-agent model to collaborate and adapt to the changing environment, enabling efficient resource allocation and improved system performance. Moreover, we implement HydraChain and conduct experiments on a high-performance server equipped with 48 cores and 125GB of memory. Our comprehensive experiments, comparing HydraChain with DQN-Based, SAC-Based and SPRING, reveal that our solution outperforms state-of-theart solutions by achieving a notable 22% increase in transaction throughput and a 5.2% reduction in workload imbalance across shards.

The application scenarios of drone collaboration are rapidly expanding, such as the low-altitude economy and wildfire protection. Blockchain-based drone collaboration requires a consensus mechanism to ensure efficient and secure consistency among large-scale distributed nodes. However, the existing consensus mechanism has problems with poor fault tolerance of topology and rigid proposal concurrency. To this end, this paper proposes AirBFT, an efficient and robust consensus mechanism for large-scale drone collaboration. First, this paper designs a new four-layer network topology, using upper-member and lower-member communication, while ensuring the maximum 1/3 resilience and fanout of √N. Secondly, this paper proposes a dynamic pipelining algorithm to adjust the parallelism of proposals according to the real-time network status. Finally, this paper proposes a committee sampling technology based on the EigenTrust algorithm to reduce the impact of the malicious behavior of Byzantine nodes. Experiments based on the public consensus framework show that compared with Kauri and HotStuff, the proposed AirBFT reduces transaction confirmation delay by 58%, the throughput is increased by 1.9 times, and it can ensure efficient operation with a 1/3 Byzantine node ratio.

Blockchain offers distinct advantages in terms of data credibility and provenance certification, and its fusion with Internet of Things (IoT) technology holds great promise. Nevertheless, IoT environments are marked by extensive node networks and intricate communication patterns, especially the sensing environment. The conventional blockchain consensus mechanism, hampered by its heavy reliance on computing resources and communication bandwidth, faces difficulties in ensuring seamless data exchange among IoT edge servers. The issues encountered by state-of-the-art Byzantine Fault Tolerance (BFT) consensus include: (i) high communication complexity between nodes; and (ii) the detrimental impact of Byzantine behavior on system performance. To overcome the above problems, we propose the lightweight blockchain consensus called AntB, firstly introducing the concept of sampling into the consensus and significantly reducing the number of participating consensus nodes from$N$to$n$, which lowers the consensus complexity to$\mathbf{2\cdot O(n)+O(N)}$. We design a dynamic reputation mechanism so that Byzantine nodes cannot control the sampling set to affect the activity of the consensus in the long term. When implementing AntB, we address three significant technical challenges: (i) to determine the optimal sample size, we propose a sampling calculation method based on statistical confidence intervals, where the sample size is primarily determined by the chosen confidence level and margin of error; (ii) to prevent Byzantine behavior, we devise a weighted random sampling mechanism utilizing reputation coefficients based on edge servers’ behaviors; and (iii) to maintain consensus activity and consistency after sampling, we propose the consensus mechanism for partial sampling and global verification to avert potential issues. We implement AntB and conduct performance evaluations in a server with 32 cores and 64GB of memory. The evaluation results indicate that, the more nodes participating in the process of consensus, the better the performance of AntB will be. Especially, compared to HotStuff, AntB has a 24.94% higher success rate and Transactions Per Second (TPS) can improve by 102.10% when the number of nodes is 300.

In recent years, sharding technology has been frequently applied in blockchain systems to increase scalability. However, when new shards are added, the system may result in significant overhead in terms of computing and networking since the data allocation approach is incompatible with dynamic changes in shards. Currently, S-Store, the state-of-the-art sharding solution built on the account model, has a high re-computing latency when growing shard numbers and an unbalanced sharded data distribution after growth. To address these issues, this paper presents Tangram, an efficient and balanced dynamic storage extension approach for sharding blockchain systems. Tangram reduces system extension overhead and latency while ensuring a balanced shard distribution. In implementing Tangram, we tackle three main technical challenges as follows. (1) Designing a novel state tree structure for the storage and maintenance of sharding state data. We introduce the Jump Merkle Tree (JMT) based on the Merkle Tree, which integrates node migration and orderliness. (2) Presenting a protocol to be compatible with dynamic shard scenarios. We devise a shard addition protocol to improve system extension availability and decrease shard extension delay. (3) Proposing an approach to guarantee system longevity after extension. We first devise algorithms for the state tree to eradicate invalid states after system expansion. Furthermore, we introduce a shard reduction protocol to enhance system storage extension support in complex scenarios, such as cleaning up inactive states to avoid bloating the state tree. We conduct extensive experiments to evaluate the performance of Tangram. Experiment results demonstrate that Tangram outperforms existing solutions, showing reduced latency and superior data balance. When compared to the state-of-the-art sharding storage solution, Tangram decreases the transaction execute time by up to 87.84%, the state data migration by more than approximately 74%, and achieves up to 7.63x improvement in the standard deviation of sharding data balance.

Asynchronous Complete Secret Sharing (ACSS) in Byzantine fault-tolerant systems has become one of the essential building blocks in multiple threshold cryptosystems. However, current ACSS schemes scale poorly due to high communication costs, which are quadratic in the number of participants n. In this paper, we propose a new scheme ALCES to reduce such communication costs from O(n2) to O(cn) with a negligible probability of failure ${e^{ - \frac{c}{{18}}}}$, while guaranteeing completeness and agreement properties. The key point of ALCES is to sample c parties to construct a committee, which then verifies and distributes the encrypted shares to other parties. Additionally, we introduce a new mechanism, referred to as secret labels in ALCES, by encoding the information of labels in polynomial coefficients. This mechanism allows an arbitrary string to act as the label, binding it to a specific secret while efficiently ensuring security and privacy with minimal communication cost. Experimental results show that our technique reduces the overall communication cost in a single sharing process by 66% and 83% for very large quantities, such as 4096 and 8192 parties, respectively, when compared with prior work.

With the rapid expansion of blockchain data, the demand for data exchange between chains has grown significantly. Authenticated queries have become one of the crucial methods for retrieving on-chain data due to their efficient performance and ability to ensure data security. However, existing intra-chain query approaches either face substantial maintenance overhead or exhibit low query efficiency, when dealing with the explosive growth of data in cross-chain scenarios; while current cross-chain query approaches suffer from issues including limited query types and poor scalability. To this end, this paper takes the lead to propose a novel generalized framework named CubeChain which provides various query types for intra- and cross-chain authenticated queries. We propose a highly scalable authenticated data structure (ADS) named Cube as the core structure of our framework which excels in achieving high performance while minimizing maintenance overhead by establishing data bridges between vertexes. When implementing CubeChain, we address two challenges: (i) implementing lightweight verification while supporting various query types by using a two-layer hashing structure, and (ii) further improving the query efficiency by suppressing vertexes. We substantiate the superior performance of Cube in terms of query efficiency, update overhead, and scalability through theoretical analysis. Finally, we implement the CubeChain framework based on the open-source Fabric v2.2. Real experiments with YCSB benchmark demonstrate that, compared with the state-of-the-art Bs+tree-based ADSs in MSTDB and SEBDB, our query performance improved by 23.75x in intra-chain scenarios and 10.72x in cross-chain scenarios, while maintaining a 30% reduction of cross-chain query load.

Blockchain systems widely employ Byzantine fault-tolerant (BFT) protocols to ensure consistency. Improving BFT protocols’ throughput is crucial for large-scale blockchain systems. Frontier protocols face crucial problems: (i) the binary dilemma between leader bottleneck in star-based linear communication and compromised resilience in tree-based sublinear communication; and (ii) 2- or 3-round protocols restrict the phase number of one proposal, thereby limiting the scalability and parallelism of the pipeline. To overcome the above problems, this paper proposes Crackle, the first sector-based pipelined BFT protocol with a sublinear communication complexity, for a throughput improvement of consensus protocol with max resilience of (N-1)/3. We propose a sector-based communication mode to disseminate messages from the leader to a subset of replicas in each phase to accelerate consensus and split the traditional two-round protocol into 2κ phases to increase the basic pipeline scale. When implementing Crackle, we address two technical challenges: (i) to ensure Quorum Certificate (QC) validation during continuous κ phases, we design a voteMap field within each block, and verify QC by the aggregation of continuous κ voteMaps; and (ii) to achieve pipeline decoupling among shorter phases, we propose a vote-appending mechanism that accelerates the leader’s transition to the next phase. We provide comprehensive theoretical proof of the correctness of Crackle, including safety and liveness. Moreover, we implement Crackle based on a public BFT framework and deploy it on 64 cloud servers. Real experimental results reveal that Crackle achieves up to 10.36x higher throughput compared with state-of-the-art BFT protocols such as Kauri and Hotstuff.

Byzantine fault-tolerant (BFT) consensus ensures system consistency in the presence of malicious replicas and is widely adopted in blockchain systems. To enhance scalability and throughput, recent advancements incorporate multiple leaders into BFT consensus. However, employing multiple leaders results in significant resource wastage in terms of storage, bandwidth, and CPU usage, attributable to transaction redundancy. Conversely, to eliminate duplication, the resilience in Byzantine settings is compromised. To bridge this gap, we propose PeterHofe, a novel ring-based collaborative transaction packaging method, aiming to maintain resource efficiency and minimize Byzantine leader influence, thereby reducing transaction latency and improving system robustness. PeterHofe extends the concept of partitioning transaction hash space into buckets, establishing many-to-many mappings between replicas and buckets to diminish Byzantine replica control. When implementing PeterHofe, we address the following two challenges. 1) To improve resistance to Byzantine censorship, we design a permutation-based ring structure with accompanying correctness proofs and mathematical analyses; 2) To further reduce transaction duplication, we introduce a Prophecy-Implementation mechanism with analyzed malicious behaviors. We implement PeterHofe on top of the latest and representative work, Narwhal and Tusk. Experimental results demonstrate that PeterHofe can achieve low resource waste and high system robustness simultaneously. Specifically, PeterHofe's resource waste rate is near 5~17% in general cases, which is a 20-fold reduction compared to the Random-based Strategy; compared with the state-of-the-art Hash-based Partitioning Strategy, the proportion of maliciously controlled transactions is reduced by at least 66%, leading to a latency decrease of up to 75%.

Owing to the attractive properties of decentralization, unforgeability, transparency, and traceability, blockchain is increasingly being used in various scenarios such as supply chain and public services, where massive Spatial-Temporal-Keywords (STK) transactions need to be packaged. However, due to the multi-dimensionality and randomness of STK transactions, existing solutions fail to enable queries in a verifiable and efficient way for blockchains storing multidimensional transactions. To this end, this article takes the first step to propose an authenticated and efficient query approach in hybrid blockchain systems consisting of on-chain and off-chain parts. We first design a data structure named MRK-Tree in the block body, which organizes STK transactions for efficient nodes pruning of both kNN and range queries. Then we propose an improved block header, which improves the efficient pruning of blocks on the basis of ensuring the authentication of query results. Also, we design a cross-block searching algorithm named Efficient Block Pruning (EBP) and intra-block searching algorithms named Authenticated kNN/Range Query (AKQ/ARQ) to accelerate authenticated queries for multiple MRK-Trees in the hybrid blockchain systems. Authentication mechanisms are proposed to ensure the soundness and completeness of query results. Rigorous security analysis validates the practicability of the proposed approach. We build a blockchain prototype to comprehensively evaluate the performance of proposed query schemes. Extensive evaluation results with real datasets reveal that our approach can ensure authenticated queries, meanwhile improving the time efficiency by up to 36.45x and space efficiency by up to 4 orders of magnitude compared with the well-known benchmark query schemes.

The consensus mechanism plays a pivotal role in guaranteeing the security and consistency of blockchain systems and substantially affects system performance. However, an increasing number of blockchain nodes degrade the consensus performance dramatically because of the high communication complexity in traditional consensus mechanisms. In this paper, we propose NS-consensus, a secure node-sampling blockchain consensus mechanism reducing the communication complexity significantly. The key novelty lies in the sampling of blockchain nodes so that the leader only needs to interact with the sampling nodes in each consensus epoch. However, NS-consensus imposes two challenges in determining an optimal sample size and denying malicious proposals. To address the challenges, we determine the sample size under the constraints of a confidence level and a margin of error to enhance communication efficiency without compromising system security. Furthermore, we design a mechanism to enable the leader to interact with all blockchain nodes in the last consensus phase, ensuring the denial of malicious proposals. The extensive experimental results indicate that NS-consensus outperforms the state-of-the-art with up to 175.1% higher system throughput and 79.9% lower time overhead in the sampling phases.

The rapid development of the blockchain leads to a blowout of on-chain transactions, contracts, and currencies, which will further accelerate the increase of data. The existing blockchain systems typically support exact transaction queries, which, however, cannot satisfy the QoS requirements with intelligent adjustment in the blockchain systems. To this end, this paper takes the first step to define and address the practically important problem of transaction cardinality estimation for QoS-adjustable intelligent blockchain systems. We first establish a mathematical relationship between the bit string and transaction cardinality. Thus, we can leverage the number of leading 1s of the obtained bit string to estimate the transaction cardinality. We then improve the block header and body with a corresponding search algorithm to access bit strings in blocks. We also propose an estimation protocol with intelligent adjustable QoS to support accuracy-guaranteed and efficiency-optimized estimation. Finally, we design an authentication scheme and guarantee the reliability of our protocol through rigorous theoretical derivation. When achieving the transaction cardinality estimation in blockchain, two technical challenges need to be addressed. (i) To ensure efficient, verifiable, and overhead-saving bit string accessing mechanism in blockchain, we propose the Merkle Cardinality Tree (MCT) and target block filtering mechanism based on Bloom Filter (BF) in off-chain and improve on-chain block header by joining the abstract of MCT and BF. (ii) To improve estimation efficiency while guaranteeing accuracy requirements in hybrid blockchain scheme, we propose a Dynamic One-round Sampling-based cardinality Estimation (DOSE) protocol and integrate BF-DOSE to intelligently accelerate estimation. We build MCT in Ethereum and store the MCT Root in the block header for estimation authentication. Extensive experiments reveal that our BF-DOSE protocol can well satisfy various accuracy and efficiency requirements of QoS-adjustable intelligent blockchain systems, and is one to two orders of magnitude faster compared with benchmark schemes.