Sharding is considered a promising solution for scaling blockchain systems. However, most existing sharding systems have not considered the dynamics of the environment when making a sharding strategy, including the change of pending transactions, the leaving and joining of participants, and malicious attacks, which could cause performance instability and security issues. To address it, in this paper, we propose an intelligent and efficient dynamic sharding technology to advance the blockchain system performance and security. We first propose a formal and general evaluation framework for blockchain sharding in a dynamic environment, and conclude an optimization target for the system performance and security. To achieve a long-term benefit for the optimization target, a deep reinforcement learning (DRL)-based sharding approach has been proposed to intelligently make optimal sharding strategies. Next, we propose an adaptive resharding protocol to efficiently reduce the overhead introduced by dynamic sharding. Our experimental results illustrate that our proposed dynamic sharding in a simulation testbed can achieve 2.8 times transactions per second compared to traditional static sharding systems, and guarantee high security in a dynamic environment.

Based on multilingual pre-trained models, cross-lingual transfer with prompt learning has shown promising effectiveness, where soft prompt learned in a source language is transferred to target languages for downstream tasks, particularly in the low-resource scenario. To efficiently transfer soft prompt, we propose a novel framework, Multilingual Prompt Translator (MPT), where a multilingual prompt translator is introduced to properly process crucial knowledge embedded in prompt by changing language knowledge while retaining task knowledge. More concretely, we first train prompt in source language and employ translator to translate it into target prompt. Besides, we extend an external corpus as auxiliary data, on which an alignment task for predicted answer probability is designed to convert language knowledge, thereby equipping target prompt with multilingual knowledge. In few-shot settings on XNLI, MPT demonstrates superiority over baselines by remarkable improvements. MPT is more prominent compared with vanilla prompting when transferring to languages quite distinct from source language. Code is available at https://github.com/qiuxiaoyu9954/MPT.

Pre-trained vision-language models (VLMs) have shown remarkable generalization capabilities via prompting, which leverages VLMs as knowledge bases to extract information beneficial for downstream tasks. However, existing methods primarily employ uni-modal prompting, which only engages a uni-modal branch, failing to simultaneously adjust vision-language (V-L) features. Additionally, the one-pass forward pipeline in VLM encoding struggles to align V-L features that have a huge gap. Confronting these challenges, we propose a novel method, Progressive Multi-modal conditional Prompt Tuning (ProMPT). ProMPT exploits a recurrent structure, optimizing and aligning V-L features by iteratively utilizing image and current encoding information. It comprises an initialization and a multi-modal iterative evolution (MIE) module. Initialization is responsible for encoding images and text using a VLM, followed by a feature filter that selects text features similar to image. MIE then facilitates multi-modal prompting through class-conditional vision prompting, instance-conditional text prompting, and feature filtering. In each MIE iteration, vision prompts are obtained from filtered text features via a vision generator, promoting image features to focus more on target object during vision prompting. The encoded image features are fed into a text generator to produce text prompts that are more robust to class shifts. Thus, V-L features are progressively aligned, enabling advance from coarse to exact prediction. Extensive experiments are conducted in three settings to evaluate the efficacy of ProMPT. The results indicate that ProMPT outperforms existing methods on average across all settings, demonstrating its superior generalization and robustness. Code is available at https://github.com/qiuxiaoyu9954/ProMPT.

Building on top of blockchain, payment channel networks-backed (PCNs) cryptocurrencies emerge as a promising solution for a mobile payment system with fewer intermediaries, more security, higher speed, and lower cost. A key problem for PCN is payment channel rebalancing, that is, finding a set of circular transactions that restore a PCN with skewed channel balances back into an equilibrium state. However, existing practice on payment channel rebalancing either has a hard limit on the problem size or tends to fall into local optimum. To address these challenges, we propose DRL-PCR, aDeepReinforcementLearning-basedPaymentChannelRebalancing algorithm. On one hand, DRL-PCR leverages the strong approximation ability of deep neural networks to handle large problem spaces. On the other hand, DRL-PCR decomposes the rebalancing problem into a sequence of decision-making problems and progressively builds the final solution. By aiming to find a globally optimized solution and solving the long-term optimization model of DRL, DRL-PCR is superior to greedy-based algorithms and can mitigate the risk of getting trapped in a local optimum. In particular, payment channel rebalancing typically involves dealing with graph-structured data, where the major obstacle lies in understanding the sophisticated circular dependencies between payment channels and routing paths. DRL-PCR achieves this by encoding the input data with a novel graph neural network-based model and capturing the circular dependencies through a customized message passing process. In addition, considering the distributed nature of PCN, DRL-PCR uses a leadership election protocol to elect leaders for decision-making. Evaluations on the historical data of two real-world PCNs demonstrate that DRL-PCR can restore the PCN to a more balanced state and improve the transaction throughput and success ratios by up to 2.1x and 1.6x, respectively.

Payment channel networks (PCNs) are considered as a prominent solution for scaling blockchain, where users can establish payment channels and complete transactions in an off-chain manner. However, it is non-trivial to schedule transactions in PCNs and most existing routing algorithms suffer from the following challenges: 1) one-shot optimization, 2) privacy-invasive channel probing, 3) vulnerability to DoS attacks. To address these challenges, we propose a privacy-aware transaction scheduling algorithm with defence against DoS attacks based on deep reinforcement learning (DRL), namely PTRD. Specifically, considering both the privacy preservation and long-term throughput into the optimization criteria, we formulate the transaction-scheduling problem as a Constrained Markov Decision Process. We then design PTRD, which extends off-the-shelf DRL algorithms to constrained optimization with an additional cost critic-network and an adaptive Lagrangian multiplier. Moreover, considering the distribution nature of PCNs, in which each user schedules transactions independently, we develop a distributed training framework to collect the knowledge learned by each agent so as to enhance learning effectiveness. With the customized network design and the distributed training framework, PTRD achieves a good balance between the optimization of the throughput and the minimization of privacy risks. Evaluations show that PTRD outperforms the state-of-the-art PCN routing algorithms by 2.7%–62.5% in terms of the long-term throughput while satisfying privacy constraints.

Blockchain technology has gained popularity owing to the success of cryptocurrencies such as Bitcoin and Ethereum. Nonetheless, the scalability challenge largely limits its applications in many real-world scenarios. Off-chain payment channel networks (PCNs) have recently emerged as a promising solution by conducting payments through off-chain channels. However, the throughput of current PCNs does not yet meet the growing demands of large-scale systems because: 1) most PCN systems only focus on maximizing the instantaneous throughput while failing to consider network dynamics in a long-term perspective; 2) transactions are re-actively routed in PCNs, in which intermediate nodes only passively forward every incoming transaction. These limitations of existing PCNs inevitably lead to channel imbalance and the failure of routing subsequent transactions. To address these challenges, we propose a novel proactive look-ahead algorithm (PLAC) that controls transaction flows from a long-term perspective and proactively prevents channel imbalance. In particular, we first conduct a measurement study on two real-world PCNs to explore their characteristics in terms of transaction distribution and topology. On that basis, we propose PLAC based on deep reinforcement learning (DRL), which directly learns the system dynamics from historical interactions of PCNs and aims at maximizing the long-term throughput. Furthermore, we develop a novel graph convolutional network-based model for PLAC, which extracts the inter-dependency between PCN nodes to consequently boost the performance. Extensive evaluations on real-world datasets show that PLAC improves state-of-the-art PCN routing schemes w.r.t the long-term throughput from 6.6% to 34.9%.

Mobile edge computing (MEC) is a promising solution to support resource-constrained devices by offloading tasks to the edge servers. However, traditional approaches (e.g., linear programming and game-theory methods) for computation offloading mainly focus on the immediate performance, potentially leading to performance degradation in the long run. Recent breakthroughs regarding deep reinforcement learning (DRL) provide alternative methods, which focus on maximizing the cumulative reward. Nonetheless, there exists a large gap to deploy real DRL applications in MEC. This is because: 1) training a well-performed DRL agent typically requires data with large quantities and high diversity, and 2) DRL training is usually accompanied by huge costs caused by trial-and-error. To address this mismatch, we study the applications of DRL on the multi-user computation offloading problem from a more practical perspective. In particular, we propose a distributed and collective DRL algorithm called DC-DRL with several improvements: 1) a distributed and collective training scheme that assimilates knowledge from multiple MEC environments, which not only greatly increases data amount and diversity but also spreads the exploration costs, 2) an updating method called adaptive n-step learning, which can improve training efficiency without suffering from the high variance caused by distributed training, and 3) combining the advantages of deep neuroevolution and policy gradient to maximize the utilization of multiple environments and prevent the premature convergence. Lastly, evaluation results demonstrate the effectiveness of our proposed algorithm. Compared with the baselines, the exploration costs and final system costs are reduced by at least 43 and 9.4 percent, respectively.

To overcome the limitations on the scalability of current blockchain systems, sharding is widely considered as a promising solution that divides the network into multiple disjoint groups processing transactions in parallel to improve throughput while decreasing the overhead of communication, computation, and storage. However, most existing blockchain sharding systems adopt a static sharding policy that cannot efficiently deal with the dynamic environment in the blockchain system, i.e., joining and leaving of nodes, and malicious attack. This paper presents SkyChain, a novel dynamic sharding-based blockchain framework to achieve a good balance between performance and security without compromising scalability under the dynamic environment. We first propose an adaptive ledger protocol to guarantee that the ledgers can merge or split efficiently based on the dynamic sharding policy. Then, to optimize the sharding policy under dynamic environment with high dimensional system states, a deep reinforcement learning-based sharding approach has been proposed, the goals of which include: 1) building a framework to evaluate the blockchain sharding systems from the aspects of performance and security; 2) adjusting the re-sharding interval, shard number and block size to maintain a long-term balance of the system’s performance and security. Experimental results show that SkyChain can effectively improve the performance and security of the sharding system without compromising scalability under the dynamic environment in the blockchain system.

With the rapid development of the Internet of Things (IoT) in the era of big data, the amount of collected data has increased dramatically. Data are one of the most important commodities in IoT. To maximize the utility of the collected data, it is crucial to design an open IoT data market that enables data owners and consumers to carry out data trading securely and efficiently. To address the challenge of security presented by an untrusted and nontransparent data market, we propose an edge/cloud-computing-assisted, blockchain-enhanced data market framework to support secure and efficient IoT data trading, with a particular focus on an optimal pricing mechanism. In this mechanism, an authorized market-agency works as a scheduler, determining the win-owner and its pricing strategy to the consumer. We formulate a two-stage Stackelberg game to solve the pricing and purchasing problem of the data consumer and the market-agency. In the first stage of the game, the market-agency gives the win-owner and its pricing strategy. In the second stage, the data consumer decides on its purchasing quantity of data. We consider competition between data owners and propose a competition-enhanced pricing scheme (CPS). We apply backward induction to analyze the subgame perfect equilibrium at each stage for both independent and CPSs. Lastly, we validate the existence and uniqueness of Stackelberg equilibrium, and the numerical results show the efficiency of the CPS.