VPFL:A verifiable privacy-preserving federated learning scheme for edge computing systems

Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

In the Internet of Things(IoT)based system,the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems(UCS).The UCS necessitates heterogeneity,management level,and data transmission for distributed users.Simultaneously,security remains a major issue in the IoT-driven UCS.Besides,energy-limited IoT devices need an effective clustering strategy for optimal energy utilization.The recent developments of explainable artificial intelligence(XAI)concepts can be employed to effectively design intrusion detection systems(IDS)for accomplishing security in UCS.In this view,this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System(BXAI-IDCUCS)model.The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment.The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization(EADSO)algorithm to accomplish this.Besides,deep neural network(DNN)is employed for detecting and classifying intrusions in the IoT network.Lastly,blockchain technology is exploited for secure inter-cluster data transmission processes.To ensure the productive performance of the BXAI-IDCUCS model,a comprehensive experimentation study is applied,and the outcomes are assessed under different aspects.The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%,a packet loss rate of 0.71%,a throughput of 92.95 Mbps,energy consumption of 0.0891 mJ,a lifetime of 3529 rounds,and accuracy of 99.38%.

The Decoherence-Free in Multi-Coupled Quantum Computing System

A subdynamics theory framework for describing multi coupled quantum computing systems is presented first. A general kinetic equation for the reduced system is given then, enabling a sufficient condition to be formulated for constructing a pure coherent quantum computing system. This reveals that using multi coupled systems to perform quantum computing in Rigged Liouville Space opens the door to controlling or eliminating the intrinsic de coherence of quantum computing systems.

Task Offloading Decision in Fog Computing System

Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.

Performance Analysis of Cooperative NOMA Based Intelligent Mobile Edge Computing System

In this manuscript, a cooperative non-orthogonal multiple access based intelligent mobile edge computing(NOMA-MEC) communication system is constructed in detail. The nearby user is viewed as a decoding and forwarding relay, which can assist a distant user in offloading tasks to the intelligent MEC server. Then, the closed-form expressions of offloading outage probability for a pair of users are derived in detail to evaluate the performance of the cooperative NOMA-MEC system. Furthermore, the approximate expressions of offloading outage probability are provided in the high signal-to-noise ratio region. Based on the asymptotic analyses, the diversity order of distant user and nearby user is n+m+1 and n+1, respectively. The system throughput and energy efficiency of cooperative NOMA-MEC are analyzed in delay-limited transmission mode. Numerical results show that 1) Cooperative NOMA-MEC is better than orthogonal multiple access(OMA) in terms of offload performance;2) The offload performance of cooperative NOMA-MEC system improves as the number of transmission task decreases;and 3) Cooperative NOMA-MEC performs better than OMA in energy efficiency.

Integration of Communication and Computing in Blockchain-Enabled Multi-Access Edge Computing Systems

Blockchain and multi-access edge com-puting(MEC)are two emerging promising tech-nologies that have received extensive attention from academia and industry.As a brand-new information storage,dissemination and management mechanism,blockchain technology achieves the reliable transmis-sion of data and value.While as a new computing paradigm,multi-access edge computing enables the high-frequency interaction and real-time transmission of data.The integration of communication and com-puting in blockchain-enabled multi-access edge com-puting networks has been studied without a systemat-ical view.In the survey,we focus on the integration of communication and computing,explores the mu-tual empowerment and mutual promotion effects be-tween the blockchain and MEC,and introduces the resource integration architecture of blockchain and multi-access edge computing.Then,the paper sum-marizes the applications of the resource integration ar-chitecture,resource management,data sharing,incen-tive mechanism,and consensus mechanism,and ana-lyzes corresponding applications in real-world scenar-ios.Finally,future challenges and potentially promis-ing research directions are discussed and present in de-tail.

Proximal Policy Optimization-Based Committee Selection Algorithm in Blockchain-Enabled Mobile Edge Computing Systems

To cope with the low latency requirements and security issues of the emerging applications such as Internet of Vehicles(Io V)and Industrial Internet of Things(IIo T),the blockchain-enabled Mobile Edge Computing(MEC)system has received extensive attention.However,blockchain is a computing and communication intensive technology due to the complex consensus mechanisms.To facilitate the implementation of blockchain in the MEC system,this paper adopts the committee-based Practical Byzantine Fault Tolerance(PBFT)consensus algorithm and focuses on the committee selection problem.Vehicles and IIo T devices generate the transactions which are records of the application tasks.Base Stations(BSs)with MEC servers,which serve the transactions according to the wireless channel quality and the available computing resources,are blockchain nodes and candidates for committee members.The income of transaction service fees,the penalty of service delay,the decentralization of the blockchain and the communication complexity of the consensus process constitute the performance index.The committee selection problem is modeled as a Markov decision process,and the Proximal Policy Optimization(PPO)algorithm is adopted in the solution.Simulation results show that the proposed PPO-based committee selection algorithm can adapt to the system design requirements with different emphases and outperforms other comparison methods.

Model of Security Evaluation of Infrastructure as a Service Layer of Cloud Computing System

At present,most providers of cloud computing mainly provide infrastructures and services of infrastructure as a service(IaaS).But there is a serious problem that is the lack of security standards and evaluation model of IaaS.After analyzing the vulnerabilities performance of IaaS cloud computing system,the mapping relationship was established between the vulnerabilities of IaaS and the nine threats of cloud computing which was released by cloud security alliance(CSA).According to the mapping relationship,a model for evaluating security of IaaS was proposed which verified the effectiveness of the model on OpenStack by the analytic hierarchy process(AHP) and the fuzzy evaluation method.

Formal Protection Architecture for Cloud Computing System

Cloud computing systems play a vital role in national security. This paper describes a conceptual framework called dualsystem architecture for protecting computing environments. While attempting to be logical and rigorous, formalism method is avoided and this paper chooses algebra Communication Sequential Process.

DCCS:A General-Purpose Distributed Cryptographic Computing System

Distributed cryptographic computing system plays an important role since cryptographic computing is extremely computation sensitive. However, no general cryptographic computing system is available. Grid technology can give an efficient computational support for cryptographic applications. Therefore, a general-purpose grid-based distributed computing system called DCCS is put forward in this paper. The architecture of DCCS is simply described at first. The policy of task division adapted in DCCS is then presented. The method to manage subtask is further discussed in detail. Furthermore, the building and execution process of a computing job is revealed. Finally, the details of DCCS implementation under Globus Toolkit 4 are illustrated.

Efficient Multi-User for Task Offloading and Server Allocation in Mobile Edge Computing Systems

Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud server.To conserve energy as well as maintain quality of service,low time complexity algorithm is proposed to complete task offloading and server allocation.In this paper,a multi-user with multiple tasks and single server scenario is considered for small network,taking full account of factors including data size,bandwidth,channel state information.Furthermore,we consider a multi-server scenario for bigger network,where the influence of task priority is taken into consideration.To jointly minimize delay and energy cost,we propose a distributed unsupervised learning-based offloading framework for task offloading and server allocation.We exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization problems.To further reduce time cost and achieve near-optimal performance,we use convolutional neural networks to process mass data based on fully connected networks.Numerical results show that the proposed algorithm performs better than other offloading schemes,which can generate near-optimal offloading decision timely.

A Generic Service Architecture for Secure Ubiquitous Computing Systems

The development of ubiquitous computing systems benefits tremendously from the service-oriented computing concept in seamless interoperation of heterogeneous devices. However, architectures, services interfaces and network implementation of the existing service-oriented systems differ case by case. Furthermore, many systems lack the capability of being applied to resource constrained devices, for example, sensors. Therefore, we propose a standardized approach to present a service to the network and to access a networked service, which can be adopted by arbitrary types of devices. In this approach, services are specified and exposed through a set of standardized interfaces. Moreover, a virtual community concept is introduced to determine a secure boundary within which services can be freely discovered, accessed and composed into applications;a hierarchical management scheme is presented which enables the third party management of services and their underlying resources. In this way, application control logic goes into the network and environment context is dealt with intelligently by the system. A prototype system is developed to validate our ideas. Results show the feasibility of this open distributed system software architecture.

Autonomic Software Component QoS Matchmaking Algorithm Based on Fidelity Factor in Agent-Based Autonomic Computing System

Autonomic software component (ASC) QoS matchmaking problem for autonomic element has been taken as one of the most important issue in field of autonomic computing based on agent. Aimed at overcoming drawbacks such as subjec-tiveness and unfairness, and improving the self-configuration capability for autonomic element, we introduce evalua-tion mechanism of confidence of individual QoS attributes during ASC QoS matchmaking, i.e., fidelity factor for each attribute, and propose an ASC QoS matchmaking algorithm based on fidelity factor. Simulation experiments demon-strate that our proposed algorithm performs best performance in terms of QoS than other existing algorithms, and has better compromise between attribute quality and users’ evaluation when selecting ASC.

Computation Offloading Algorithms in Mobile Edge Computing System: A Survey

With the rapid development of the internet of things (IoT), the number of devices that can connect to the network has exploded. More computation intensive task appear on mobile terminals, and mobile edge computing has emerged. Computation offloading technology is a key technology in mobile edge computing. This survey reviews the state of the art of computation offloading algorithms. It was classified into three categories: computation offloading algorithms in MEC system with single user, computation offloading algorithms in MEC system with multiple users, computation offloading algorithms in MEC system with enhanced MEC server. For each category of algorithms, the advantages and disadvantages were elaborated, some challenges and unsolved problems were pointed out, and the research prospects were forecasted.

ATSSC:An Attack Tolerant System in Serverless Computing

Serverless computing is a promising paradigm in cloud computing that greatly simplifies cloud programming.With serverless computing,developers only provide function code to serverless platform,and these functions are invoked by its driven events.Nonetheless,security threats in serverless computing such as vulnerability-based security threats have become the pain point hindering its wide adoption.The ideas in proactive defense such as redundancy,diversity and dynamic provide promising approaches to protect against cyberattacks.However,these security technologies are mostly applied to serverless platform based on“stacked”mode,as they are designed independent with serverless computing.The lack of security consideration in the initial design makes it especially challenging to achieve the all life cycle protection for serverless application with limited cost.In this paper,we present ATSSC,a proactive defense enabled attack tolerant serverless platform.ATSSC integrates the characteristic of redundancy,diversity and dynamic into serverless seamless to achieve high-level security and efficiency.Specifically,ATSSC constructs multiple diverse function replicas to process the driven events and performs cross-validation to verify the results.In order to create diverse function replicas,both software diversity and environment diversity are adopted.Furthermore,a dynamic function refresh strategy is proposed to keep the clean state of serverless functions.We implement ATSSC based on Kubernetes and Knative.Analysis and experimental results demonstrate that ATSSC can effectively protect serverless computing against cyberattacks with acceptable costs.

Data and Model Driven Task Offloading Strategy in the Dynamic Mobile Edge Computing System

Mobile Edge Computing(MEC)provides communication and computational capabilities for the industrial Internet,meeting the demands of latency-sensitive tasks.Nevertheless,traditional model-driven task offloading strategies face challenges in adapting to situations with unknown network communication status and computational capabilities.This limitation becomes notably significant in complex industrial networks of high-speed railway.Motivated by these considerations,a data and model-driven task offloading problem is proposed in this paper.A redundant communication network is designed to adapt to anomalous channel states when tasks are offloaded to edge servers.The link switching mechanism is executed by the train according to the attributes of the completed task.The task offloading optimization problem is formulated by introducing data-driven prediction of communication states into the traditional model.Furthermore,the optimal strategy is achieved by employing the informer-based prediction algorithm and the quantum particle swarm optimization method,which effectively tackle real-time optimization problems due to their low time complexity.The simulations illustrate that the data and model-driven task offloading strategy can predict the communication state in advance,thus reducing the cost of the system and improving its robustness.

Research on General-Purpose Brain-Inspired Computing Systems

Brain-inspired computing is a new technology that draws on the principles of brain science and is oriented to the efficient development of artificial general intelligence(AGI),and a brain-inspired computing system is a hierarchical system composed of neuromorphic chips,basic software and hardware,and algorithms/applications that embody this tech-nology.While the system is developing rapidly,it faces various challenges and opportunities brought by interdisciplinary research,including the issue of software and hardware fragmentation.This paper analyzes the status quo of brain-inspired computing systems.Enlightened by some design principle and methodology of general-purpose computers,it is proposed to construct"general-purpose"brain-inspired computing systems.A general-purpose brain-inspired computing system refers to a brain-inspired computing hierarchy constructed based on the design philosophy of decoupling software and hardware,which can flexibly support various brain-inspired computing applications and neuromorphic chips with different architec-tures.Further,this paper introduces our recent work in these aspects,including the ANN(artificial neural network)/SNN(spiking neural network)development tools,the hardware agnostic compilation infrastructure,and the chip micro-archi-tecture with high flexibility of programming and high performance;these studies show that the"general-purpose"system can remarkably improve the efficiency of application development and enhance the productivity of basic software,thereby being conductive to accelerating the advancement of various brain-inspired algorithms and applications.We believe that this is the key to the collaborative research and development,and the evolution of applications,basic software and chips in this field,and conducive to building a favorable software/hardware ecosystem of brain-inspired computing.

IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)technologies.The network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing capacities.The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption.The system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT context.This study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial applications.Experimental results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn O.Moreover,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy consumption.This research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

Technical Perspective:Research on General-Purpose Brain-Inspired Computing Systems

Brain-inspired computing is a popular research area with the potential to advance our understanding of brain function,artificial intelligence,and next-generation computing machinery.Often referred to as"neuromorphic",these systems and algorithms hope to harness mechanisms present in brains to make step changes in perfor-mance over regular von Neumann based approaches[1].