With the widespread adoption of multi-view data in numerous fields, multi-view unsupervised feature selection (MUFS) has made notable strides in both feature pruning and missing-view completion. Nonetheless, existing MUFS methods typically rely on centralized servers, which cannot meet real-world demands for privacy preservation and distributed learning, and they often suffer from suboptimal solution and weak convergence guarantees. To address these challenges, IMUFFS, an incomplete multi-view unsupervised federated feature selection via cooperative particle swarm optimization (CPSO) and tensor-aligned learning (TAL) is proposed. Specifically, each client executes CPSO-TAL at two stages: (i) an external optimization phase that involves a CPSO, inspired by the co-evolutionary mechanism of hybrid breeding optimization algorithm, performing a global search in the feature space, and (ii) an internal optimization phase that leverages TAL with imputation and CP decomposition, where CP decomposition reduces dimensionality by decomposing the original tensor into a sum of core components, to learn low-dimensional embeddings, while simultaneously updating anchor graphs and view preference weights, thereby harmonizing imputation and representation learning. On the server side, a federated aggregation strategy using adaptive normalized mutual information (NMI) weighting combines the locally optimized feature selection (FS) weights and NMI scores from clients, ensuring privacy while improving the quality of FS and convergence. Extensive experiments on multiple datasets demonstrate that IMUFFS consistently outperforms state-of-the-art methods, yielding more effective and robust FS and enhancing better missing-view completion.

The Execute-Order-Validate (EOV) framework has been used to improve the scalability of blockchains by concurrently executing transactions. However, the EOV framework also poses a critical performance issue. Specifically, when multiple transactions access the same data, only one of them can be committed eventually while the others are aborted due to the strong concurrency control restriction. This inefficiency makes the EOV framework far from practicality since there always exist hotspot variables that can be frequently accessed in real-world scenarios, such as the Fungible Token (FT) and Non-Fungible Token (NFT) online marketplace. In this paper, we propose RapidSnail, a novel EOV framework that enables transactions to execute based on the uncommitted data to reduce the transaction abort rate in such scenarios with hotspot variables. We first propose a new read-write set representation and a concurrency execution schedule algorithm in the execution phase to maintain the concurrent efficiency. Then we propose an effect-based conflict graph construction algorithm in the order phase to handle the conflict transactions based on the new read-write set. Finally, we propose a concurrent commitment schedule algorithm to adopt the new read-write set to validate the transactions concurrently in the validation phase. Our experiment results show that RapidSnail increases the throughput by at least 4× compared to the state-of-the-art EOV framework under high contention workload. More specifically, RapidSnail reduces the abort rate by 50%, and achieves at least 4× speedup in the order phase and 2.94× speedup in the validation phase over the existing EOV frameworks.

Blockchain sharding has been deemed a promising solution that can substantially improve blockchain scalability. However, developers must overcome two major technical challenges to implement a sharded blockchain. The first challenge is the high cross-shard transaction ratio in blockchain shards. This issue significantly degrades the throughput of a sharded blockchain. The second challenge is the imbalanced workloads across blockchain shards. In a blockchain with imbalanced workloads, some busy shards have to handle an overwhelming number of transactions and thus become congested. Facing these two challenges, a dilemma is that it is difficult to guarantee a lowcross-shard transaction ratioand maintain thebalanced workloadsacross all shards, simultaneously. We believe that a fine-grained account allocation strategy can address this dilemma. To this end, we formulate the tradeoff between these two metrics as a network-partition problem. We then solve this problem by proposing a sharding protocol, namedShardCutter, which includes the following two crucial components: a community-aware account partition algorithm and a fine-tuned account migration mechanism. Finally, experimental results demonstrate that the proposed protocol outperforms other baselines in terms of throughput, makespan, cross-shard transaction ratio, and the workload balance of shards’ transaction pool.

Accurate identification of DNA-binding proteins (DBPs) is crucial for understanding various biological processes. While deep learning has advanced this field, many methods still rely on complex, hand-crafted feature engineering. In this paper, we propose a novel and efficient framework for DBP prediction. We first leverage a state-of-the-art protein language model, ESM-1b, to generate fixed-length, information-rich vector representations for protein sequences, completely bypassing the need for manual feature design. For classification, we introduce a Local Sparse Representation-based Classifier (LSRC). This classifier operates on the assumption that a protein's representation lies in a low-dimensional subspace spanned by its local neighbors in the feature space. By constructing a small, adaptive dictionary for each test sample, LSRC achieves both high accuracy and re-markable computational efficiency. We evaluated our framework on two benchmark datasets and a newly constructed dataset. The results demonstrate that our method achieves new state-of-the-art performance, outperforming existing methods in accuracy, MCC. and other metrics.

Optimal energy management between microgrids (MGs) and battery swapping stations (BSSs) offers significant economic benefits. However, existing works face challenges in formulating optimal interaction strategies between MGs and BSSs, due to the temporal-spatial uncertainty of distributed renewables and emerging loads, as well as incomplete information. This article addresses the energy transaction problem between multi-MGs and multi-BSSs using a hybrid multiagent deep reinforcement learning approach to minimize operation costs. Specifically, a hierarchical transactive energy management community is introduced to facilitate energy exchange between MMGs and BSSs, considering different stakeholder interests. The problem is modeled as a partially observable Markov game. The proposed hybrid algorithm, combining multiagent proximal policy optimization (MAPPO) and double deep Q-network (DDQN), handles the continuous scheduling of MGs and the discrete operations of BSSs. Numerical results show that, averaged over the baselines, the proposed MAPPO-DDQN reduces 13.71% of MGs' operation costs and increases 14.62% of BSSs' profit.

Sharding is a promising solution to scale blockchain by separating the system into multiple shards to process transactions in parallel. However, due to state separation and shard isolation, it is still challenging to efficiently support smart contracts on a blockchain sharding system where smart contracts can interact with each other, involving states maintained by multiple shards. Specifically, existing sharding systems adopt a costly multi-step collaboration mechanism to execute smart contracts, resulting in long latency and low throughput. This article proposesSparrow, a blockchain sharding protocol achieving one-step execution for smart contracts. To break shard isolation, inspired by non-local hotspot data caching in traditional databases, we propose a new idea ofinter-shard caching, allowing a shard to prefetch and cache frequently accessed contract states of other shards. The miner can thus use the inter-shard cache to pre-execute a pending transaction, retrieve all its contract invocations, and commit it to multiple shards in one step. Particularly, we first propose a speculative dispersal cache synchronisation mechanism for efficient and secure cache synchronization across shards in Byzantine environments. Then, we propose a multi-branch exploration mechanism to solve the rollback problem during the optimistic one-step execution of contract invocations with dependencies. We also present a series of conflict resolution mechanisms to decrease the rollback caused by inherent transaction conflicts. We implement prototypes forSparrowand existing sharding systems, and the evaluation shows thatSparrowimproves the throughput by$2.44\times$and reduces the transaction latency by 30% compared with the existing sharding systems.

Federated Learning (FL) is a promising distributed machine learning framework that allows clients to collaboratively train a global model without data leakage. The synchronous FL suffers from the inefficient training caused by the slow-speed clients, which are called stragglers. Though asynchronous FL can well address the efficiency challenge, it induces massive system overheads and model degradation. As a framework considering the trade-off between synchronous and asynchronous FL, semi-asynchronous FL gains increasing attention. However, when clients' resources become a bottleneck, an unfair client scheduling may degrade global training accuracy and increase system overheads, especially in heterogeneous environments. In this paper, we propose Libra, which is a new FL framework aiming to achieve fair client scheduling in semi-asynchronous FL mode. Libra restricts devices that train too fast according to the model discrepancy. Furthermore, it selects stale local models according to the number of participating into FL training by clients. Additionally, Libra conducts a biased client selection while considering clients' resources and local losses. The experimental results show that Libra outperforms other baselines in terms of convergence accuracy, system overhead, and fairness of client participation. We also conduct an ablation study to further prove the effectiveness of Libra. In brief, Libra can achieve fair client scheduling and reduce inefficient local updates.

Payment channel network (PCN) routing is crucial for ensuring a high system throughput and transaction (TX) success ratio. However, designing PCN routing across IoT blockchain shards is not straightforward. First, the sharded architecture isolates information among multiple shards, increasing difficulties and costs associated with channel probing. Second, the dynamic nature of IoT, e.g., frequent changes in PCN topology and channel states, makes traditional mathematical routing approaches inefficient and even invalid. These challenges inevitably result in a high TX failure ratio and low throughput. This paper presents a novel cross-shard PCN routing IoT blockchain framework that optimizes TX scheduling to maximize long-term throughput and success ratio. Specifically, we propose an efficient cross-shard PCN routing protocol that distinctly categorizes channels into intra-shard and inter-shard types, simplifying cross-shard channel probing and reducing costs. Then, to optimize PCN routing policies in dynamic sharding environments, we propose a deep reinforcement learning algorithm, which includes: 1) multi-agent collaborative learning for the view of incomplete information across shards; 2) a two-layer network architecture to reduce computational complexity on resource-constrained IoT devices. Experiment results show that the proposed cross-shard routing improves 48.4 % of the TX success ratio averaged over baselines, which is 1.56 times of average throughput compared with other routing algorithms in cross-shard PCNs.

Developing effective platforms for economic energy management is considered a pivotal issue in the field of electric vehicles (EVs). To implement a cost-effective energy management platform (EMP), developers must overcome two major challenges. The first challenge lies in the environmental dynamic nature, such as EV location, energy price fluctuations, storage levels, and parking availability at charging stations. This causes most traditional one-shot optimizations to fail. The second challenge pertains to the lack of regulation in EV energy exchanges. To address these challenges, we propose a cost-aware two-stage EMP based on blockchain and deep reinforcement learning (DRL), namely, TEMP. Specifically, TEMP first develops a sharding-based blockchain energy management framework, which guarantees trust, security, privacy, traceability, and accountability without the need for intermediaries. Then, considering the complex and high-dimensional environment, TEMP devises a two-stage cooperative scheduling scheme by combining ant colony optimization (ACO) with proximal policy optimization (PPO) to enhance learning effectiveness. Evaluations show that TEMP outperforms the two state-of-the-art baselines by 12.3% and 4.4% in terms of long-term profits while reducing costs by 6.7% and 2.8%, respectively. Moreover, energy transaction efficiency can be ensured when the EV number of blockchain networks is gradually increased.

Sharding technologies allow the Internet of Things (IoT) to deploy blockchains in large-scale applications with good scalability. However, conventional sharding strategies in IoT blockchain are highly restricted because most IoT devices are dynamic and heterogeneous. They fail to partition and reconfigure shards with a fine-balanced tradeoff between throughput and security. Therefore, we propose SmartChain, which is a dynamic and self-adaptive sharding framework devised for making sharding decisions on the IoT blockchain featured with dynamics and heterogeneity. Specifically, we elaborate on how SmartChain performs reconfiguration and provide a quantitative analysis of shard performance. We then formulate the long-term tradeoff of throughput and security as a Markov decision process. Considering the nature of time-varying devices (e.g., amount of computing power, location), we develop a Transferable Proximal Policy Optimization (PPO) with Demonstrations algorithm, namely TPPOD, to help quickly reconfigure shards when the environment changes. Thus, based on current state, SmartChain can adaptively and dynamically select shard number, partition structure, and primary selection mode. Evaluations show that SmartChain enables high throughput and low risk of security, and reduces 70% of the training time averaged over baselines. Our implementation of TPPOD is 8.3 times of average system reward compared with the PPO-based sharding strategy with uniform sampling.

Social data produced from widely emerged social media activities are expected to promote information dissemination and engagement, or even make business intelligence more powerful. However, the recent increase in social media incidents of illegal surveillance and data breaches raises questions about the current data ownership model, in which centralized applications collect and control large amounts of user data. In this paper, we present SocialChain, which is a decentralized social data storage and sharing system based on blockchain that decouples user data and social applications to return data ownership to the user. We adopt Personal Data Store to extend off-chain storage for the social data, set up an identity establishment mechanism that can support WebID-based authentication functions using a unique identity assignment (i.e., WebID) as well as certificateless cryptography, and design a general framework that leverages smart contracts to help securely store and share social data in an automated manner. We develop a software prototype based on Ethereum and conduct case studies to test the effects of the adopted techniques on the performance. Experimental results show that SocialChain can provide easy-to-use interfaces while introducing relatively low latency, cost, and overhead and that it can support real-world social media applications.

Blockchain-based cryptocurrencies have developed rapidly in recent years, however, scalability is one of the biggest challenge. Payment channel networks (PCNs) are one of the important solutions to blockchain scalability and routing is the most critical problem in PCN. Routing algorithms in PCNs have evolved fast and achieved high throughput. However, most of these routing algorithms are designed from the perspective of technical feasibility, and few algorithms focus on the incentives of each off-chain participant, especially the economic incentives for intermediate routing nodes. Besides, due to the highly dynamic nature of off-chain channel deposits, existing routing algorithms rely heavily on channel deposit probing in order to ensure high throughput. In this article, we design routing algorithms from an incentive perspective to improve the profit of intermediate nodes and use deep learning to reduce the dependency of off-chain routing on channel deposit probing. Our experiments show that under the same model, MDRL-IR can increase the profit of intermediate nodes by up to 1.87x and increase the throughput by up to 2.0x compared to the state-of-the-art routing algorithm, while ensuring that the user routing cost per unit throughput remains unchanged. Moreover, approximate performance can be achieved when deposit probing is greatly reduced.