Reduce Communication Overhead Research Articles

FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features

We introduce a novel federated learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters. Conventional federated learning approaches are inefficient for cross-silo problems because they require the exchange of messages for gradient updates at every iteration, and raise security concerns over sharing such messages during learning. We propose a <i>Federated Stochastic Block Coordinate Descent (FedBCD)</i> algorithm, allowing each party to conduct multiple local updates before each communication to effectively reduce communication overhead. Under a practical security model, we show that parties cannot infer others&#x0027; exact raw data (<i>&#x201C;deep leakage&#x201D;</i>) from collections of messages exchanged in our framework, regardless of the number of communication to be performed. Further, we provide convergence guarantees and empirical evaluations on a variety of tasks and datasets, demonstrating significant improvement inefficiency.

Design Principles of Secure Certificateless Signature and Aggregate Signature Schemes for IoT Environments

Certificateless cryptography resolves the certificate management problem of public-key cryptography and the key-escrow problem of identity-based cryptography. An aggregate signature scheme which allows to aggregate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> distinct signatures on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> distinct messages of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> distinct signers into a single signature reduces communication overhead and computational cost. Due to the suitability of certificateless signature (CLS) and certificateless aggregate signature (CLAS) schemes for IoT environments, similar CLS and CLAS schemes have been proposed for a long time and, despite their security proofs, they have been attacked and modified to prevent the attacks. Even now, similar design methods and similar attacks on the schemes are being repeated. In order to prevent the similar attacks on the schemes, it is necessary to analyze their causes and vulnerabilities. In this paper, we first show that recently proposed five CLS and CLAS schemes are insecure against universal forgery attacks, type I attacks, type II attacks or malicious-but-passive-KGC attacks. We discuss their security flaws, causes and countermeasures. We then present design principles to prevent various algebraic attacks including our attacks. The design principles will help in the construction of secure CLS and CLAS schemes against the previous attacks and potential attacks.

A Virtual Machine Migration Strategy Based on the Relevance of Services against Side-Channel Attacks

With the rapid development of Internet of Things technology, a large amount of user information needs to be uploaded to the cloud server for computing and storage. Side-channel attacks steal the private information of other virtual machines by coresident virtual machines to bring huge security threats to edge computing. Virtual machine migration technology is currently the main way to defend against side-channel attacks. VM migration can effectively prevent attackers from realizing coresident virtual machines, thereby ensuring data security and privacy protection of edge computing based on the Internet of Things. This paper considers the relevance between application services and proposes a VM migration strategy based on service correlation. This strategy defines service relevance factors to quantify the degree of service relevance, build VM migration groups through service relevance factors, and effectively reduce communication overhead between servers during migration, design and implement the VM memory migration based on the post-copy method, effectively reduce the occurrence of page fault interruption, and improve the efficiency of VM migration.

Deep learning-based linear defects detection system for large-scale photovoltaic plants based on an edge-cloud computing infrastructure

Linear defects detection of photovoltaic (PV) modules plays a key role in the health assessment in PV plants. However, the conventional defects diagnosis is mainly carried out manually and hence is inefficient or even infeasible in practice. With the recent advances in computing and communication technologies, different forms of edge computing facilities are designed with less human intervention and a non-destructive inspection in the industry. However, the assessment of PV plants using electroluminescence images faces some fundamental challenges. This paper proposed an automatic linear defects detection system for large-scale PV plants based on an edge-cloud computing framework. A novel deep learning-based PV defects detection algorithmic solution is developed considering the trade-off between detection performance and computational complexity through allocating the computing tasks across the edge devices, edge server and cloud server. The proposed solution is evaluated through experiments using real-world data and the numerical results demonstrate its effectiveness and accuracy of PV defect detection as well as the reduction of communication overhead by decreasing the size of EL images.

A Traffic-Aware Federated Imitation Learning Framework for Motion Control at Unsignalized Intersections with Internet of Vehicles

The wealth of data and the enhanced computation capabilities of Internet of Vehicles (IoV) enable the optimized motion control of vehicles passing through an intersection without traffic lights. However, more intersections and demands for privacy protection pose new challenges to motion control optimization. Federated Learning (FL) can protect privacy via model interaction in IoV, but traditional FL methods hardly deal with the transportation issue. To address the aforementioned issue, this study proposes a Traffic-Aware Federated Imitation learning framework for Motion Control (TAFI-MC), consisting of Vehicle Interactors (VIs), Edge Trainers (ETs), and a Cloud Aggregator (CA). An Imitation Learning (IL) algorithm is integrated into TAFI-MC to improve motion control. Furthermore, a loss-aware experience selection strategy is explored to reduce communication overhead between ETs and VIs. The experimental results show that the proposed TAFI-MC outperforms imitated rules in the respect of collision avoidance and driving comfort, and the experience selection strategy can reduce communication overheads while ensuring convergence.

Third Party Public Auditing Scheme for Cloud Storage

Abstract: Thus the new auditing scheme has been developed by considering all these requirements. It consist of three entities: data owner, TPA and cloud server. The data owner performs various operations such as splitting the file to blocks, encrypting them, generating a hash value for each, concatenating it and generating a signature on it. The TPA performs the main role of data integrity check. It performs activities like generating hash value for encrypted blocks received from cloud server, concatenating them and generates signature on it. It later compares both the signatures to verify whether the data stored on cloud is tampered or not. It verifies the integrity of data on demand of the users. The cloud server is used only to save the encrypted blocks of data. This proposed auditing scheme make use of AES algorithm for encryption, SHA-2 for integrity check and RSA signature for digital signature calculation. In this philosophy, users of cloud storage services no longer physically maintain direct control over their data, which makes data security one of the major concerns of using cloud. Existing research work already allows data integrity to be verified without possession of the actual data file. When the verification is done by a trusted third party, this verification process is also called data auditing, and this third party is called an auditor. As a result, every small update will cause re-computation and updating of the authenticator for an entire file block, which in turn causes higher storage and communication overheads. In this paper, we provide a formal analysis for possible types of fine-grained data updates and propose a scheme that can fully support authorized auditing and fine-grained update requests. Basedon our scheme, we also propose an enhancement that can dramatically reduce communication overheads for verifying small updates Keywords: Cloud computing, big data, data security, authorized auditing, fine-grained dynamic data update

Low-latency edge cooperation caching based on base station cooperation in SDN based MEC

With the increase of mobile terminal equipment and network mass data, users have higher requirements for delay and service quality. To reduce user access latency and more effectively cache diverse content in the edge network, a low-latency edge caching method is proposed. The cache model based on base station cooperation is established and the delay in different transmission modes is considered. Finally, the problem of minimizing latency is transformed into a problem of maximizing cache reward, and a greedy algorithm based on the original dual interior point is used to obtain the strategy of the original problem. Meanwhile, in order to improve service quality and balance communication overhead and migration overhead, a migration method based on balanced communication overhead and migration overhead is proposed. The model that balances communication overhead and migration overhead is established, and the reinforcement learning method is used to obtain a migration scheme that maximizes accumulated revenue. Comparison results show that our caching method can enhance the cache reward and reduce delay. Meanwhile, the migration algorithm can increase service migration revenue and reduce communication overhead.

Privacy‐preserving and efficient user matching based on attribute encryption in mobile social networks

SummaryWith the rapid popularity of social networking platforms, users can be matched when sharing their profiles. However, there is a risk of leakage of sensitive user information during the user matching process, which leads to the lack of user privacy protection. In this paper, we propose a privacy protection scheme based on the encryption of hidden attributes during user matching in mobile social networks, which uses linear secret sharing scheme (LSSS) as the access structure based on ciphertext policy attribute‐based encryption (CP‐ABE), and the match server can perform friend recommendation by completing bi‐directional attribute matching determination without disclosing user attribute information. In addition, the use of selective keywords protects the privacy of requesters and publishers in selecting keywords and selecting plaintext attacks. The scheme reduces the encryption and decryption overhead for users by dividing encryption into a preparation phase and an online phase and shifting most of the decryption overhead from the requester to the match server. The experimental results show that the scheme ensures user privacy while effectively reducing communication overhead.

Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data

Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection, for detection, classification, and prediction of future events, from different locations while maintaining a strict privacy constraint. Due to privacy concerns and critical communication bottlenecks, it can become impractical to send the FL updated models to a centralized server. Thus, this paper studies the potential of hierarchical FL in Internet of Things (IoT) heterogeneous systems. In particular, we propose an optimized solution for user assignment and resource allocation over hierarchical FL architecture for IoT heterogeneous systems. This work focuses on a generic class of machine learning models that are trained using gradient-descent-based schemes while considering the practical constraints of non-uniformly distributed data across different users. We evaluate the proposed system using two real-world datasets, and we show that it outperforms state-of-the-art FL solutions. Specifically, our numerical results highlight the effectiveness of our approach and its ability to provide 4–6% increase in the classification accuracy, with respect to hierarchical FL schemes that consider distance-based user assignment. Furthermore, the proposed approach could significantly accelerate FL training and reduce communication overhead by providing 75–85% reduction in the communication rounds between edge nodes and the centralized server, for the same model accuracy.

Data Dissemination for Industry 4.0 Applications in Internet of Vehicles Based on Short-term Traffic Prediction

As a key use case of Industry 4.0 and the Smart City, the Internet of Vehicles (IoV) provides an efficient way for city managers to regulate the traffic flow, improve the commuting performance, reduce the transportation facility cost, alleviate the traffic jam, and so on. In fact, the significant development of Internet of Vehicles has boosted the emergence of a variety of Industry 4.0 applications, e.g., smart logistics, intelligent transforation, and autonomous driving. The prerequisite of deploying these applications is the design of efficient data dissemination schemes by which the interactive information could be effectively exchanged. However, in Internet of Vehicles, an efficient data scheme should adapt to the high node movement and frequent network changing. To achieve the objective, the ability to predict short-term traffic is crucial for making optimal policy in advance. In this article, we propose a novel data dissemination scheme by exploring short-term traffic prediction for Industry 4.0 applications enabled in Internet of Vehicles. First, we present a three-tier network architecture with the aim to simply network management and reduce communication overheads. To capture dynamic network changing, a deep learning network is employed by the controller in this architecture to predict short-term traffic with the availability of enormous traffic data. Based on the traffic prediction, each road segment can be assigned a weight through the built two-dimensional delay model, enabling the controller to make routing decisions in advance. With the global weight information, the controller leverages the ant colony optimization algorithm to find the optimal routing path with minimum delay. Extensive simulations are carried out to demonstrate the accuracy of the traffic prediction model and the superiority of the proposed data dissemination scheme for Industry 4.0 applications.

A source-driven reinforcement learning-based Data reply strategy to reduce communication overhead in Named Data Networks (NDN)

A source-driven reinforcement learning-based Data reply strategy to reduce communication overhead in Named Data Networks (NDN)

Rough Estimator Based Asynchronous Distributed Super Points Detection on High Speed Network Edge

Super points detection plays an important role in network research and application. With the increase of network scale, distributed super points detection has become a hot research topic. The key point of super points detection in a multi-node distributed environment is how to reduce communication overhead. Therefore, this paper proposes a three-stage communication algorithm to detect super points in a distributed environment, Rough Estimator based Asynchronous Distributed super points detection algorithm (READ). READ uses a lightweight estimator, the Rough Estimator (RE), which is fast in computation and takes less memory to generate candidate super points. Meanwhile, the famous Linear Estimator (LE) is applied to accurately estimate the cardinality of each candidate super point, so as to detect the super point correctly. In READ, each node scans IP address pairs asynchronously. When reaching the time window boundary, READ starts three-stage communication to detect the super point. This paper proves that the accuracy of READ in a distributed environment is no less than that in the single-node environment. Four groups of 10 Gb/s and 40 Gb/s real-world high-speed network traffic are used to test READ. The experimental results show that READ not only has high accuracy in a distributed environment, but also has less than 5% of communication burden compared with existing algorithms.

Performance Modelling and Analysis of IoT Based Edge Computing Policies

In today’s era, the acceptance of IoT-based edge devices is growing exponentially, which creates challenges of data acquisition, processing, and communication. In the edge computing paradigm, intelligence is shifted from the center to the edge by performing specific processing and prediction locally. Existing methods are not well computed with timely manner and it reduces the security level by overfitting problem. A strategy based on reducing communication resources between sensors and edge devices is the prime focus of this investigation. It uses a predictive model-based policy at edge devices for the reconstruction of not delivered context vector. A new hybrid Averaged Exponential Smoothening policy proposed is based on the current context vectors as well as a smoothing vector to reduce reconstruction error and improve the percentage of communication. It is observed that if we send data only when there is a marginal change in data then we can reduce communication overhead as well as keep reconstruction error low. This policy would be suitable for IoT-based edge computing applications for the smart city such as Smart Home, Healthcare, and Intelligent traffic to delivers the power of AI.

Routing with Face Traversal and Auctions Algorithms for Task Allocation in WSRN.

Four new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in wireless sensor and robot networks based on the greedy-face-greedy (GFG) routing extended with auctions are proposed in this paper. In this paper, we assume that all robots are mobile, and after the event is found (reported by sensors), the goal is to allocate the task to the most suitable robot to act upon the event, using either distance or the robots’ remaining energy as metrics. The proposed algorithms consist of two phases. The first phase of algorithms is based on face routing, and we introduced the parameter called search radius (SR) at the end of this first phase. Routing is considered successful if the found robot is inside SR. After that, the second phase, based on auctions, is initiated by the robot found in SR trying to find a more suitable one. In the simulations, network lifetime and communication costs are measured and used for comparison. We compare our algorithms with similar algorithms from the literature (k-SAAP and BFS) used for the task assignment. RFTA2 and RFTA2GE feature up to a seven-times-longer network lifetime with significant communication overhead reduction compared to k-SAAP and BFS. Among our algorithms, RFTA2GE features the best robot energy utilization.

Distributed smooth optimisation with event-triggered proportional-integral algorithms

In this paper, we consider the distributed smooth optimisation problem of multi-agent systems over undirected connected communication topologies. The objective is to minimise the global objective function, which is the sum of local private objective functions, by exchanging and computing local information among each agent or with its neighbourhoods. To avoid continuous communication among agents and reducing communication overheads, we propose an event-triggered distributed optimisation algorithm based on a proportional-integral control strategy. We first show that the developed algorithm does not exhibit Zeno behaviour. Then, it is shown that the proposed algorithm exponentially converges to an exact global minimiser under the restricted secant inequality condition. This condition admits a more general class of the global objective function since it does not require the convexity of the global objective function and the global minimisers are not required to be unique. Theoretical results are illustrated by numerical simulations.

Decentralized Online Learning With Compressed Communication for Near-Sensor Data Analytics

Near-sensor data analytics advocates processing data locally near their sources, rather than gathering them for centralized processing. It can reduce communication costs and is particularly suitable for networked sensor systems whose data are geo-distributed. As most sensors and associated devices have limited computing power, it is desirable for them to collaborate, especially in a decentralized manner, so that the workload can be distributed and no single point becomes a bottleneck. In this letter, we present a decentralized machine learning algorithm with communication compression capability that can serve as the core of a near-sensor data analytics task. Owing to its online nature and reduced communication overhead, the proposed method is particularly suitable for real-world sensor network systems with energy and bandwidth constraints.

Simulation of machine learning-based 6G systems in virtual worlds

Digital representations of the real world are being used in many applications, such as augmented reality. 6G systems will not only support use cases that rely on virtual worlds but also benefit from their rich contextual information to improve performance and reduce communication overhead. This paper focuses on the simulation of 6G systems that rely on a 3D representation of the environment, as captured by cameras and other sensors. We present new strategies for obtaining paired MIMO channels and multimodal data. We also discuss trade-offs between speed and accuracy when generating channels via ray tracing. We finally provide beam selection simulation results to assess the proposed methodology.

A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter

Distributed estimation and tracking of interested objects over wireless sensor networks (WSNs) is a hot research topic. Since network topology possesses distinctive structural parameters and plays an important role for the performance of distributed estimation, we first formulate the communication overhead reduction problem in distributed estimation algorithms as the network topology optimization in this paper. The effect of structural parameters on the algebraic connectivity of a network is overviewed. Moreover, aiming to reduce the communication overhead in Kalman consensus filter (KCF)-based distributed estimation algorithm, we propose a network topology optimization method by properly deleting and adding communication links according to nodes’ local structural parameters information, in which the constraint on the communication range of two nodes is incorporated. Simulation results show that the proposed network topology optimization method can effectively improve the convergence rate of KCF algorithm and achieve a good trade-off between the estimate error and communication overhead.

Secure Multi-Subinterval Data Aggregation Scheme with Interval Privacy Preservation for Vehicle Sensing Systems

Road side units (RSUs) can act as fog nodes to perform data aggregation at the edge of network, which can reduce communication overhead and improve the utilization of network resources. However, because the RSU is public infrastructure, this feature may bring data security and privacy risks in data aggregation. In this paper, we propose a secure multi-subinterval data aggregation scheme, named SMDA, with interval privacy preservation for vehicle sensing systems. Specifically, our scheme combines the [Formula: see text] encoding theory and proxy re-encryption to protect interval privacy, this can ensure that the interval information is only known by the data center, and the RSU can classify the encrypted data without knowing the plaintext of the data and interval information. Meanwhile, our scheme employs the Paillier homomorphic encryption to accomplish data aggregation at the RSU, and the Identity-based batch authentication technology to solve authentication and data integrity. Finally, the security analysis and performance evaluations illustrate the safety and efficiency of our scheme.

Enhancing the efficiency and scalability of blockchain through probabilistic verification and clustering

Blockchain is a disruptive technique that finds many applications in FinTech, IoT, and token economy. Because of the asynchrony, the competitive mining, and the indeterministic block propagation delay in networks, forks in the blockchain occur frequently, which not only waste a lot of computing resources but also result in potential security issues. This issue will greatly affect the efficiency of blockchain networks. In the meantime, when blockchain networks expand, the storage data for each node will be increasing dramatically. Participates are about to face the problem of storage limitation. Blockchain is hard to scale. This paper introduced PvScheme, a probabilistic verification scheme that could effectively reduce the block propagation delay and reduce the occurrence of blockchain forks. We further enhanced the security of PvScheme to provide reliable block delivery. We also analysed the resistance of PvScheme to fake blocks and double spending attacks. Furthermore, we proposed ICIStrategy, a multi-node collaborative storage strategy based on intra-cluster integrity. In ICIStrategy, we divided all participates into several clusters. Each cluster requires holding all data of the network, whereas a node within the cluster does not need to maintain data integrity. It aims to solve the storage pressure by reducing the amount data that each participate need to store and reduce communication overhead by collaboratively storing and verifying blocks through in-cluster nodes.We designed the experiments to study the performance of PvScheme and ICIStrategy. The experiment results illustrated that PvScheme could reduce the propagation delay of the standard protocol by 43.1% when the validation degree was 0.5. And we got the best effect when the validation degree is 0.9. It got a relatively lower forks and a relatively higher security at the same time, which achieved a compromise between the efficiency and security in the blockchain. According to the experimental results, ICIStrategy decreased effectively the storage of each node. And the larger the scale of networks, the less the storage of each node.