Abstract Federated learning (FL) is a distributed machine learning framework that coordinates clients to train models on their private datasets via a centralized server, thereby mitigating data privacy risks. However, the communication channels involved in this process are untrusted, leaving FL vulnerable to model poisoning attacks launched by adversaries through man-in-the-middle techniques. Such attacks can degrade the accuracy of the global model and ultimately cause the entire FL training process to fail. In this paper, we propose a defense mechanism that integrates secure verification with watermarking, with the primary goal of ensuring the integrity of models transmitted over communication channels and enabling highly reliable FL deployment. Our mechanism leverages a dual watermarking method: first, models are marked using specially generated samples, and then these samples are further watermarked based on a class histogram-inspired approach. This dual strategy enhances both model detection and watermark stealthiness. The key innovation of our method lies in its sensitivity to subtle tampering while imposing no loss in model accuracy. Experimental results demonstrate that our defense mechanism significantly strengthens the resilience of FL models against sophisticated model poisoning attacks, while maintaining high accuracy and reliability.

Clustered Federated Learning (CFL) has emerged as an effective solution to address data heterogeneity in traditional Federated Learning (FL). However, the intrinsic cluster-based structure of CFL introduces new privacy risks, making it more vulnerable to client-level inference attacks. In this paper, we propose a novel client-level data reconstruction attack based on Conditional Generative Adversarial Networks (cGANs), which exploits intra-cluster similarities to enhance the quality of reconstructed private data. Unlike prior works, our attack requires only partial access to a victim’s model updates through passive eavesdropping, thereby reflecting a more realistic threat model in decentralized and resource-constrained environments such as the Internet of Things (IoT). To mitigate this threat, we develop a lightweight and adaptive defense mechanism grounded in Local Differential Privacy (LDP). Our design incorporates dynamic privacy budget decay, selective layer-wise noise injection, and real-time similarity-guided adaptation. This approach achieves a favorable privacy-utility trade-off while explicitly addressing the computational, communication, and latency constraints inherent in IoT environments. Experimental results demonstrate that our proposed attack improves reconstruction similarity by up to 20% compared with existing baselines, while the defense reduces attack success rate by 27.2% with only a 3.3% accuracy drop. Moreover, it significantly lowers computational cost—reducing FLOPs by 42.7%, memory usage by 23.4%, and DP noise processing time by 45.5%—without introducing additional communication overhead. These findings highlight the underestimated privacy vulnerabilities in CFL and underscore the necessity of efficient, context-aware defense strategies.

Abstract Federated learning (FL), celebrated for its privacy‐preserving features, has been revealed by recent studies to harbour security vulnerabilities that jeopardize client privacy, particularly through data reconstruction attacks that enable adversaries to recover original client data. This study introduces a client‐level handwriting forgery attack method for FL based on generative adversarial networks (GANs), which reveals security vulnerabilities existing in FL systems. It should be stressed that this research is purely for academic purposes, aiming to raise concerns about privacy protection and data security, and does not encourage illegal activities. Our novel methodology assumes an adversarial scenario wherein adversaries intercept a fraction of parameter updates via victim clients’ wireless communication channels, then use this information to train GAN for data recovery. Finally, the purpose of handwriting imitation is achieved. To rigorously assess and validate our methodology, experiments were conducted using a bespoke Chinese digit dataset, facilitating in‐depth analysis and robust verification of results. Our experimental findings demonstrated enhanced data recovery effectiveness, a client‐level attack and greater versatility compared to prior art. Notably, our method maintained high attack performance even with a streamlined GAN design, yielding increased precision and significantly faster execution times compared to standard methods. Specifically, our experimental numerical results revealed a substantial boost in reconstruction accuracy by 16.7%, coupled with a 51.9% decrease in computational time compared to the latest similar techniques. Furthermore, tests on a simplified version of our GAN exhibited an average 10% enhancement in accuracy, alongside a remarkable 70% reduction in time consumption. By surmounting the limitations of previous work, this study fills crucial gaps and affirms the effectiveness of our approach in achieving high‐accuracy client‐level data reconstruction within the FL context, thereby stimulating further exploration into FL security measures.

Blockchain technology has garnered significant attention from academia and industry, with scalability remaining a key challenge. Sharding is a promising solution, dividing the blockchain into smaller partitions called shards, each processing a portion of the transactions to increase throughput. This approach is critical for enabling efficient Proof of Stake (PoS) consensus mechanisms, as demonstrated by the transition of Dogecoin to PoS, where sharding reduces the computational burden on validators and enhances scalability. However, sharding introduces high storage redundancy, as nodes in each shard must collectively maintain a copy of the entire blockchain, imposing substantial storage pressure. To address this, segments are introduced to divide the main chain into smaller parts distributed across nodes. Existing methods, however, randomly assign segments to nodes, resulting in high costs for node setup and segment queries. This paper investigates the optimal allocation of segments within shards to minimize these costs, proposing a Segment Allocation algorithm based on Cost Clustering (SACC). Theoretical analysis and simulations demonstrate that SACC achieves lower setup, query, and total costs while maintaining security and scalability, offering a more efficient solution for sharding-based PoS blockchains like Dogecoin.

As a new federated learning(FL) paradigm, clustered federated learning (CFL) could effectively address the issue of model training accuracy loss due to different data distribution in FL. However, the introduction of the clustering process also brings new risks. Adversaries can implement model poisoning by adding crafted perturbations with clients' model parameters, potentially resulting in overall clustering failure. To tackle this problem, we propose a client detection and parameter correction framework in this paper. Our approach aims to identify malicious clients by analyzing the difference in vector parameter density distribution between malicious and benign clients. We precisely locate malicious perturbations in the parameters and recover them, enabling the server to effectively utilize benign updates for normal clustering and training within the CFL framework. Experiment results show that our defense algorithm outperforms others, consistently improving training accuracy by an average of 30% under various kinds of attacks.

Summary Vehicular edge computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks, where vehicles can choose to upload tasks to nearby edge nodes for processing. However, since the communication between vehicles and edge nodes is via wireless network, which means the channel condition is complex. Moreover, in reality, the arrival time of each vehicle task is stochastic, so efficient communication methods should be designed for VEC. As one of the key communication technologies in 5G, non‐orthogonal multiple access (NOMA) can effectively increase the number of simultaneous transmission tasks and enhance transmission performance. In this article, we design a NOMA‐based task allocation scheme to improve the VEC system. We first establish the mathematical model and divide the allocation of tasks into two processes: the transmission process and the computation process. In the transmission process, we adopt the NOMA technique to upload the tasks in batches. In the computation process, we use a high response‐ratio strategy to determine the computation order. Then we define the optimization objective as maximizing task completion rate and minimizing task energy consumption, which is an integer nonlinear problem with lots of integer variables and cannot be solved directly. Through further analysis, we design a heuristics algorithm which we name as the AECO (average energy consumption optimization) algorithm. By using the AECO, we obtain the optimal allocation strategy by constantly adjusting the optimal variables. Simulation results demonstrate that our algorithm has a significant number of advantages.

Sharding is a promising strategy to enhance blockchain scalability. However, the surge in transactions has led to heightened relations between nodes in the system, reflecting the volume of transactions between them. The increase in related nodes engaging in identical transactions across diverse shards leads to substantial cross-shard transactions, contributing to communication delays and impeding enhancements in throughput. Current methods typically employ greedy or heuristic approaches to organize nodes into shards, resulting in marginal reductions in the total relation between related nodes in different shards (i.e., the number of cross-shard transactions), while causing shard imbalance. Hence, there is a crucial need for periodic shard reorganization based on node relations to minimize the total relation between related nodes across different shards while ensuring shard balance. In this article, we investigate the reorganization of nodes into shards based on node relations in sharding blockchains, aiming to minimize the total relation between related nodes in different shards. We formulate the shard reorganization problem and introduce the shard reorganization algorithm based on the relation between nodes (SRRN) to address this issue. Theoretical analysis proves that SRRN is a$2\lambda M$-approximation algorithm, where$\lambda=({r_{\max}}/{r_{\min}})$, with$M$representing the number of shards, and$r_{\max}$and$r_{\min}$denoting the maximum and minimum nonzero relations between nodes, respectively. Simulation results demonstrate that SRRN outperforms baseline algorithms in terms of total relation, degree of relation reduction, differences in computing power between shards, cross-shard ratio, and throughput.

The vehicular edge computing (VEC) is a new paradigm that allows vehicles to offload computational tasks to base stations (BSs) with edge servers for computing. In general, the VEC paradigm uses the 5G for wireless communications, where the massive multi-input multi-output (MIMO) technique will be used. However, considering in the VEC environment with many vehicles, the energy consumption of BS may be very large. In this paper, we study the energy optimization problem for the massive MIMO-based VEC network. Aiming at reducing the relevant BS energy consumption, we first propose a joint optimization problem of computation resource allocation, beam allocation and vehicle grouping scheme. Since the original problem is hard to be solved directly, we try to split the original problem into two subproblems and then design a heuristic algorithm to solve them. Simulation results show that our proposed algorithm efficiently reduces the BS energy consumption compared to other schemes.

Privilege separation (privsep) is an effective technique for improving software’s security, but privsep involves decomposing software into components and assigning them different privileges. This is often laborious and error-prone. This paper contributes the following for applying privsep to C software: (1) a portable, lightweight, and distributed runtime library that abstracts externally-enforced compartment isolation; (2) an abstract compartmentalization model of software for reasoning about privsep; and (3) a privsep-aware Clang-based tool for code analysis and semi-automatic software transformation to use the runtime library. The evaluation spans 19 compartmentalizations of third-party software and examines: Security: 4 CVEs in widely-used software were rendered unexploitable; Approximate Effort Saving: on average, the synthesis-to-annotation code ratio was greater than 11.9 (i.e., 10 × lines of code were generated for each annotation); and Overhead: execution-time overhead was less than 2%, and memory overhead was linear in the number of compartments.

Abstract Debugging imperative network programs is a difficult task for operators as it requires understanding various network modules and complicated data structures. For this purpose, this paper presents an automated technique for repairing network programs with respect to unit tests. Given as input a faulty network program and a set of unit tests, our approach localizes the fault through symbolic reasoning, and synthesizes a patch ensuring that the repaired program passes all unit tests. It applies domain-specific abstraction to simplify network data structures and exploits function summary reuse for modular symbolic analysis. We have implemented the proposed techniques in a tool called NetRep and evaluated it on 10 benchmarks adapted from real-world software-defined network controllers. The evaluation results demonstrate the effectiveness and efficiency of NetRep for repairing network programs.

Abstract Writing classification rules to identify interesting network traffic is a time-consuming and error-prone task. Learning-based classification systems automatically extract such rules from positive and negative traffic examples. However, due to limitations in the representation of network traffic and the learning strategy, these systems lack both expressiveness to cover a range of applications and interpretability in fully describing the traffic’s structure at the session layer. This paper presents Sharingan system, which uses program synthesis techniques to generate network classification programs at the session layer. Sharingan accepts raw network traces as inputs and reports potential patterns of the target traffic in NetQRE, a domain specific language designed for specifying session-layer quantitative properties. We develop a range of novel optimizations that reduce the synthesis time for large and complex tasks to a matter of minutes. Our experiments show that Sharingan is able to correctly identify patterns from a diverse set of network traces and generates explainable outputs, while achieving accuracy comparable to state-of-the-art learning-based systems.

With the emergence and development of various computer technologies, many jobs processed in cloud computing systems consist of multiple associated tasks which follow the constraint of execution order. The task of each job can be assigned to different nodes for execution, and the relevant data are transmitted between nodes to complete the job processing. The computing or communication capabilities of each node may be different due to processor heterogeneity, and hence, a task scheduling algorithm is of great significance for job processing performance. An efficient task scheduling algorithm can make full use of resources and improve the performance of job processing. The performance of existing research on associated task scheduling for multiple jobs needs to be improved. Therefore, this paper studies the problem of multijob associated task scheduling with the goal of minimizing the jobs’ makespan. This paper proposes a task Duplication and Insertion algorithm based on List Scheduling (DILS) which incorporates dynamic finish time prediction, task replication, and task insertion. The algorithm dynamically schedules tasks by predicting the completion time of tasks according to the scheduling of previously scheduled tasks, replicates tasks on different nodes, reduces transmission time, and inserts tasks into idle time slots to speed up task execution. Experimental results demonstrate that our algorithm can effectively reduce the jobs’ makespan.

Edge computing, which sinks a large number of complex calculations into edge servers, can effectively meet the requirement of low latency and bandwidth efficiency and can be conducive to the development of the Internet of Vehicles (IoV). However, a large number of edge servers mean a big cost, especially for the 5G scenario in IoV, because of the small coverage of 5G base stations. Fortunately, coherent beamforming (CB) technology enables fast and long‐distance transmission, which gives us a possibility to reduce the number of 5G base stations without losing the whole network performance. In this paper, we try to adopt the CB technology on the IoV 5G scenario. We suppose we can arrange roadside nodes for helping transferring tasks of vehicles to the base station based on the CB technology. We first give the mathematical model and prove that it is a NP‐hard model that cannot be solved directly. Therefore, we design a heuristic algorithm for an Iterative Coherent Beamforming Node Design (ICBND) algorithm to obtain the approximate optimal solution. Simulation results show that this algorithm can greatly reduce the cost of communication network infrastructure.

In general, edge devices and edge servers in edge computing environment communicate with each other by wireless network, which put forward a high requirement for end-to-end wireless communication performance. In this paper, we propose an optimal strategy by combining the coherent beamforming (CB) technique and the successive interference cancellation (SIC) technique for improving the performance of the edge device communications. CB technique can be used for expanding the transmitter's transmitting range, while SIC technique can be used for improving the receiver's receiving ability. However, when these two techniques are used jointly, interference will occur between transmitters and receivers, which makes the CB-SIC strategy hard to be designed. We first give the mathematical model based on CB-SIC and show it is difficult to solve directly. Then, we design a heuristic algorithm called time slot loop allocation (TSLA) algorithm. TSLA is based on greedy strategy to obtain an approximate optimal solution. By using TSLA, the whole scheduling time will be divided into many time slots. In each time slot, we try to make as many edge devices as possible to transmit data to the server. These can increase the overall data throughput. In simulation, we compare CB-SIC wireless network with CB only, SIC only, and traditional multi-hop network. Simulation results show that the TSLA algorithm can improve the end-to-end communication performance in edge computing environment.

Predicting short-term urban traffic flow is a non- trivial task, for an intelligent transportation system could greatly facilitate urban transportation infrastructure construction and enhances the efficiency of traffic control. Unfortunately, urban traffic flow is influenced by numerous factors, which increases the complexity of prediction. In this paper, Multi-Grouped Least Squares Support Vector Machine (MLS-SVM) is proposed for short-term urban traffic flow prediction. In MLS-SVM, spatiotemporal factors (e.g., time, geography, and environment) are divided into different groups. Correlations between each grouped factor are then recognized. Finally, the predicted effect is optimized by combining sub- models for each group. Real-world datasets are used in the experiments of traffic flow prediction. Comparing with the rival methods (i.e., LS-SVM, Wavelet Neural Network, Multi-Factor Pattern Recognition), the simulation results demonstrated the validity and stability of MLS-SVM.

Distributed ledgers based on blockchain, such as Bitcoin and Ethereum, are known and used worldwide now. But the existing distributed ledgers are too slow for commercial requirements when compared with centralized systems like Visa. To tackle this issue, many strategies have been put forward, including GHOST, Bitcoin-NG, Sharding, State channel, Plasma, etc. But few of them is focused on the P2P layer, while the P2P layer plays a crucial role as a distributed ledger needs to be updated via block propagation process. We propose an alternative protocol named Ari, which is a P2P optimization for blockchain systems. Ari protocol makes the block propagation graph an intercrossing net rather than a unidirectional tree, thus leading to a great reduction on latency as well as a great expansion on throughput, and the security as well as scalability is still guaranteed.

The life of many wireless sensor networks is limited by their battery-based energy source. Recharging batteries from a distance by the wireless energy transferring technique could lift this restriction. However, how to deploy the mobile charging device requires further research. In this paper, we take the energy constraint of mobile wireless charger (MWC) into consideration and aim at minimizing the total energy consumption by it in recharging cycles. After formulating the optimization problem, we present the MMES-LME method based on the modified MAXMIN Ant System and equalization strategy with the constraint of MWC limited energy. The equalization strategy is presented to equalize the lifespan of all sensor nodes to avoid the untimely death of WSN and balance the consumption of MWC travelling energy and recharging energy. Our experimental results demonstrate improved performance in comparison to the greedy method and MM-LME method, which is based on MAX-MIN Ant System.

Due to the increasing usage of cloud computing applications, it is important to minimize energy cost consumed by a data center, and simultaneously, to improve quality of service via data center management. One promising approach is to switch some servers in a data center to the idle mode for saving energy while to keep a suitable number of servers in the active mode for providing timely service. In this paper, we design both online and offline algorithms for this problem. For the offline algorithm, we formulate data center management as a cost minimization problem by considering energy cost, delay cost (to measure service quality), and switching cost (to change servers’s active/idle mode). Then, we analyze certain properties of an optimal solution which lead to a dynamic programming based algorithm. Moreover, by revising the solution procedure, we successfully eliminate the recursive procedure and achieve an optimal offline algorithm with a polynomial complexity. For the online algorithm, We design it by considering the worst case scenario for future workload. In simulation, we show this online algorithm can always provide near-optimal solutions.

Though there exist ready-made DSRC/WiFi/3G/4G cellular systems for roadway communications, there are common defects in these systems for roadway safety oriented applications and the corresponding challenges remain unsolved for years, i.e., WiFi cannot work well in vehicular networks due to the high probability of packet loss caused by burst communications, which is a common phenomenon in roadway networks; 3G/4G cannot well support real-time communications due to the nature of their designs; DSRC lacks the support to roadway safety oriented applications with hard realtime and reliability requirements [1]. To solve the conflict between the capability limitations of existing systems and the ever-growing demands of roadway safety oriented communication applications, we propose a novel system design and implementation for realtime reliable roadway communications, aiming at providing safety messages to users in a realtime and reliable manner. In our extensive experimental study, the latency is well controlled within the hard realtime requirement (100ms) for roadway safety applications given by NHTSA [2], and the reliability is proved to be improved by two orders of magnitude compared with existing experimental results [1]. Our experiments show that the proposed system for roadway safety communications can provide guaranteed highly reliable packet delivery ratio (PDR) of 99% within the hard realtime requirement 100ms under various scenarios, e.g., highways, city areas, rural areas, tunnels, bridges. Our design can be widely applied for roadway communications and facilitate the current research in both hardware and software design and further provide an opportunity to consolidate the existing work on a practical and easy-configurable low-cost roadway communication platform.

In this paper we propose and analyze a localized backbone renovating algorithm (LBR) to renovate a broken backbone in the network. This research is motivated by the problem of virtual backbone maintenance in wireless ad hoc and sensor networks, where the coverage area of nodes are disks with identical radii. According to our theoretical analysis, the proposed algorithm has the ability to renovate the backbone in a purely localized manner with a guaranteed connectivity of the network, while keeping the backbone size within a constant factor from that of the minimum CDS. Both the communication overhead and computation overhead of the LBR algorithm are O(k), where k is the number of nodes broken or added. We also conduct extensive simulation study on connectivity, backbone size, and the communication/computation overhead. The simulation results show that the proposed algorithm can always keep the renovated backbone being connected at low communication/computation overhead with a relatively small backbone, compared with other existing schemes. Furthermore, the LBR algorithm has the ability to deal with arbitrary number of node failures and additions in the network.

Interference management is an important problem in wireless networks. In this paper, we focus on the successive interference cancellation (SIC) technique, and aim to design an efficient cross-layer solution to increase throughput for multi-hop wireless networks with SIC. We realize that the challenge of this problem is its mixed integer linear programming formulation, which has bunches of integer variables. In order to solve this problem efficiently, we propose an iterative framework to improve the solution for integer variables and use a linear programming to solve the problem for other variables. Our analysis indicates that the proposed algorithm is with polynomial-time complexity. Simulation results show that SIC can increase throughput of a multi-hop wireless network by around 300%.