Low Communication Cost Research Articles

Federated Learning With Unreliable Clients: Performance Analysis and Mechanism Design

Owing to the low communication costs and privacy-promoting capabilities, federated learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, with the distributed architecture, low-quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. In this article, we model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk. Specifically, we first investigate the impact on the models caused by unreliable clients by deriving a convergence upper bound on the loss function based on the gradient descent updates. Our bounds reveal that with a fixed amount of total computational resources, there exists an optimal number of local training iterations in terms of convergence performance. We further design a novel defensive mechanism, named deep neural network-based secure aggregation (DeepSA). Our experimental results validate our theoretical analysis. In addition, the effectiveness of DeepSA is verified by comparing with other state-of-the-art defensive mechanisms.

LCEDA: Lightweight and Communication-Efficient Data Aggregation Scheme for Smart Grid

Secure data aggregation for smart grid aims to protect the privacy of individual data and guarantee the utility of big data. To protect user’s privacy in data aggregation, public-key-based homomorphic encryption and masking-value-based methods are adopted in existing works. However, public-key-based homomorphic encryption causes unaffordable computation costs for smart meters (SMs), while the masking-value-based works suffer from inefficient communication. Therefore, we propose a lightweight and communication efficient data aggregation (LCEDA) scheme for smart grid. First, LCEDA allows SMs to freely form the aggregation zone at lower communication and computation costs. Second, in order to ensure forward security of individual data, LCEDA achieves efficient update of masking value share, which greatly saves the complexity and computation costs compared with the existing schemes. Third, LCEDA supports dynamic enrollment and revocation of SMs to solve the malfunction and migration of SMs and improve the scalability of the LCEDA scheme. Finally, the security of LCEDA is analyzed, and extensive performance evaluations and experiments demonstrate that LCEDA is more efficient and practical.

An enhanced hybrid ant colony optimization routing protocol for vehicular ad-hoc networks

Vehicle ad-hoc networks (VANETs) are a subclass of mobile ad hoc networks (MANETs). The VANETs communication framework is used to provide communication between moving vehicles in highway and urban road scenarios. Dynamic properties of VANETs, such as high dynamic topology, frequent route failure, high mobility of nodes, and bandwidth constraints, reduce the efficiency of routing. The long length route between source and destination affects the efficiency of the protocol in the form of high overhead, frequent disconnections, high packet loss rate, low packet delivery rate, and low throughput. In this paper, we propose an Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) to improve the efficiency of the routing process using the shortest path. The shortest path in the proposed protocol has low communication costs and the least number of hops between source and destination vehicles. The EHACORP has two phases. In phase 1, the EHACORP relies on a distance calculation method to compute the distance between vehicles. In phase 2, the source-based ant colony optimization is used to guide the ants to build a shorter path with the least number of hops to transmit data. The shortest path improves the efficiency of protocol in all aspects. The simulation results show that the EHACORP has improved packet delivery rate, throughput, end-to-end delay, routing overhead, and packet loss rate compared to Fuzzy based ant colony optimization (F-ANT), Ad hoc on-demand distance vector (AODV), ant colony optimization routing algorithm (ARA), and AntNet routing protocols.

CE-SKE: cost-effective secure key exchange scheme in Fog Federation

Security in cyberspace has become an important problem in the modern world. Security threats are a disadvantage for jobbers on cloud computing, thus using different levels of information security in different contexts, is required more than ever. In distributed systems, encryption and key exchange are used to create security. The Diffie–Hellman protocol is one of the first methods, which allows the users, two people or two organizations, to create a shared key without any prior knowledge and exchange it through an insecure communication path. This research shows that the Diffie–Hellman protocol is vulnerable to active and passive attacks. We have provided a secure key Exchange scheme Based on the Diffie–Hellman for Fog Federation. The scheme is then compared with other protocols in terms of security requirements, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is used for security analysis. The results show that the scheme is safe against active and passive attacks and has a low communication cost and a medium computation cost compared to other protocols.

An Efficient Flow Label Collector Design for High-Speed Networks

Flow label collection plays an important role in high-speed network traffic measurement. However, most existing measurement studies assume the flow labels are known a priori. Only a few methods are proposed to collect flow labels from network packet streams, which still have limitations like expensive communication cost and high missing rates. This letter addresses the limitations of the prior art by proposing an efficient flow label collector. It bounds the duplications of large flows and discards the small flows that do not matter, achieving lower missing rates at low communication cost. The experimental results based on real network traces show that, compared to the state-of-the-art methods, it can reduce about 90% flow label missing rate for medium flows or save more than 75% communication cost.

Designing Blockchain-Based Access Control Protocol in IoT-Enabled Smart-Grid System

We design a new blockchain-based access control protocol in IoT-enabled smart-grid system, called DBACP-IoTSG. Through the proposed DBACP-IoTSG, the data is securely brought to the service providers from their respective smart meters (SMs). The peer-to-peer (P2P) network is formed by the participating service providers, where the peer nodes are responsible for creating the blocks from the gathered data securely from their corresponding SMs and adding them into the blockchain after validation of the blocks using the voting-based consensus algorithm. In our work, the blockchain is considered as private because the data collected from the consumers of the SMs are private and confidential. By the formal security analysis under the random oracle model, nonmathematical security analysis and software-based formal security verification, DBACP-IoTSG is shown to be resistant against various attacks. We carry out the experimental results of various cryptographic primitives that are needed for comparative analysis using the widely used multiprecision integer and rational arithmetic cryptographic library (MIRACL). A detailed comparative study reveals that DBACP-IoTSG supports more functionality features and provides better security apart from its low communication and computation costs as compared to recently proposed relevant schemes. In addition, the blockchain implementation of DBACP-IoTSG has been performed to measure computational time needed for the varied number of blocks addition and also the varied number of transactions per block in the blockchain.

A Two-Layer IP Hopping-Based Moving Target Defense Approach to Enhancing the Security of Mobile Ad-Hoc Networks.

Mobile ad-hoc networks (MANETs) have great potential applications in military missions or emergency rescue due to their no-infrastructure, self-organizing and multi hop capability characteristics. Obviously, it is important to implement a low-cost and efficient mechanism of anti-invasion, anti-eavesdropping and anti-attack in MANETs, especially for military scenarios. The purpose of intruding or attacking a MANET is usually different from that of wired Internet networks whose security mechanism has been widely explored and implemented. For MANETs, moving target defense (MTD) is a suitable mechanism to enhance the network security, whose basic idea is to continuously and randomly change the system parameters or configuration to create inaccessibility for intruders and attackers. In this paper, a two-layer IP hopping-based MTD approach is proposed, in which device IP addresses or virtual IP addresses change or hop according to the network security status and requirements. The proposed MTD scheme based on the two-layer IP hopping has two major advantages in terms of network security. First, the device IP address of each device is not exposed to the wireless physical channel at all. Second, the two-layer IP hops with individual interval and rules to obtain enhanced security of MANET while maintaining relatively low computational load and communication cost for network control and synchronization. The proposed MTD scheme is implemented in our developed MANET terminals, providing three level of network security: anti-intrusion in normal environment, intrusion detection in offensive environment and anti-eavesdropping in a hostile environment by combining the data encryption technology.

Identifying the module structure of swarms using a new framework of network-based time series clustering

Swarm is a collective motion phenomenon whose dynamic mechanism and cooperation structure could be identified based on observations. Unmanned Aerial Vehicles (UAV) is a special artificial swarm with unique rules and structures. Therefore, corresponding identification methods need to be developed. One critical identification problem is distinguishing the swarm’s cooperation structure, which is usually clustered and grouped to achieve stability of behaviors and low communication cost. This paper proposes a framework of Overlay Network Integrated Time series clustering (ONIT) to identify the UAV swarm structures based on trajectories. The framework consists of Snapshot, Net Growth and Net Split. It can fuse with most distance functions in time series clustering, achieving high accuracy, update ability, and fault tolerance with various datasets. We create point-based and sliding window-based snapshots, allowing the framework compatible with more methods. In particular, the Dynamic Time Wrapping (DTW) correspondence in point-based snapshots shows the high scalability of the framework, and the Euclidean Distance (ED) correspondence shows that the framework can still significantly improve the accuracy while maintaining the simplicity of calculation. The test results show that the fused ONIT-clustering algorithms, especially the point-based ones, outperform original time series clustering methods separately in simulation datasets of UAV swarms and UCR repository by 28% and 27%. In summary, the proposed framework is a flexible and scalable time series clustering method that can solve various time series clustering problems especially the trajectory clustering of the UAV swarm and has great potential for general time series analysis.

An Efficient Mutual Authentication and Symmetric Key Agreement Scheme for Wireless Body Area Network

A Wireless Body Area Networks (WBANs) is a wireless network in which sensors are embedded inside the body of a human, to monitor the health of patient continuously without any constraint in his normal daily life activities. As the information from the embed sensor is transmitted through wireless network and device has a limited battery power, therefore, the assurance of security in such tiny devices related to medical patients is highly recommended. Thus, the shared information must be maintained in terms of integrity, confidentiality, non-repudiation, untraceable key establishment, and mutual authentication in WBAN. In this context, to achieve high security and efficiency in WBAN, an efficient mutual authentication and secret key agreement scheme have been proposed in this paper and also listed out some drawbacks of an existing mutual authentication and key agreement of Li et al.’s scheme. To confirm the efficiency and security, the proposed scheme has been verified using formal security analysis tool namely, ProVerif and BAN logic. The low communication and computation costs indicate that our scheme is more suitable for practical application in healthcare as compared to other existing schemes.

BD-D1Sec: Protocol of security authentication for BeiDou D1 civil navigation message based on certificateless signature

The BeiDou navigation satellite system (BDS) lacks a security authentication mechanism for BeiDou civil navigation message (BD-CNAV), which faces the threat of information spoofing attacks that may cause errors in positioning and timing information. This paper focuses on the security authentication of BeiDou D1 civil navigation message (BD-D1-CNAV) in BD-CNAV, trying to design a security authentication protocol from the perspective of information authentication. Hence, a protocol of security authentication for BD-D1-CNAV based on certificateless signature is proposed, called as BD-D1Sec protocol. For the purpose of anti-spoofing attacks on BD-D1-CNAV, BD-D1Sec protocol inserts a certificateless signature into the transmission process of BD-D1-CNAV to guarantee the integrity and authenticity of BD-D1-CNAV information without requiring certificate management, which will not reduce the availability of BD-D1-CNAV information. The simulation results show that BD-D1Sec protocol has good authentication and timeliness, a strong ability to resist spoofing attack against BD-D1-CNAV, and has the advantages of low computational complexity and communication cost.

Differentially Private Publication of Vertically Partitioned Data

In this paper, we study the problem of publishing vertically partitioned data under differential privacy, where different attributes of the same set of individuals are held by multiple parties. In this setting, with the assistance of a semi-trusted curator, the involved parties aim to collectively generate an integrated dataset while satisfying differential privacy for each local dataset. Based on the latent tree model (LTM), we present a differentially private latent tree (DPLT) approach, which is, to the best of our knowledge, the first approach to solving this challenging problem. In DPLT, the parties and the curator collaboratively identify the latent tree that best approximates the joint distribution of the integrated dataset, from which a synthetic dataset can be generated. The fundamental advantage of adopting LTM is that we can use the connections between a small number of latent attributes derived from each local dataset to capture the cross-dataset dependencies of the observed attributes in all local datasets such that the joint distribution of the integrated dataset can be learned with little injected noise and low computation and communication costs. DPLT is backed up by a series of novel techniques, including two-phase latent attribute generation (TLAG), tree index based correlation quantification (TICQ) and distributed Laplace perturbation protocol (DLPP). Extensive experiments on real datasets demonstrate that DPLT offers desirable data utility with low computation and communication costs.

Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs

We consider a distributed empirical risk minimization (ERM) optimization problem with communication efficiency and privacy requirements, motivated by the federated learning (FL) framework (Kairouz et al ., 2019). Unique challenges to the traditional ERM problem in the context of FL include $\textsf{(i)}$ need to provide privacy guarantees on clients’ data, $\textsf{(ii)}$ compress the communication between clients and the server, since clients might have low-bandwidth links, $\textsf{(iii)}$ work with a dynamic client population at each round of communication between the server and the clients, as a small fraction of clients are sampled at each round. To address these challenges we develop (optimal) communication-efficient schemes for private mean estimation for several $\ell _{p}$ spaces, enabling efficient gradient aggregation for each iteration of the optimization solution of the ERM. We also provide lower and upper bounds for mean estimation with privacy and communication constraints for arbitrary $\ell _{p}$ spaces. To get the overall communication, privacy, and optimization performance operation point, we combine this with privacy amplification opportunities inherent to this setup. Our solution takes advantage of the inherent privacy amplification provided by client sampling and data sampling at each client (through Stochastic Gradient Descent) as well as the recently developed privacy framework using anonymization, which effectively presents to the server responses that are randomly shuffled with respect to the clients. Putting these together, we demonstrate that one can get the same privacy, optimization-performance operating point developed in recent methods that use full-precision communication, but at a much lower communication cost, i.e., effectively getting communication efficiency for “free”.

(n − 2)-Fault-Tolerant Edge-Pancyclicity of Crossed Cubes CQn

As one of the most fundamental networks for parallel and distributed computation, cycle is suitable for developing simple algorithms with low communication cost. A graph [Formula: see text] is called [Formula: see text]-fault-tolerant edge-pancyclic if after deleting any faulty set [Formula: see text] of [Formula: see text] vertices and/or edges from [Formula: see text], every correct edge in the resulting graph lies in a cycle of every length from [Formula: see text] to [Formula: see text], inclusively, where [Formula: see text] is the girth of [Formula: see text], the length of a shortest cycle in [Formula: see text]. The [Formula: see text]-dimensional crossed cube [Formula: see text] is an important variant of the hypercube [Formula: see text], which possesses some properties superior to the hypercube. This paper investigates the fault-tolerant edge-pancyclicity of [Formula: see text], and shows that if [Formula: see text] contains at most [Formula: see text] faulty vertices and/or edges then, for any fault-free edge [Formula: see text] and every length [Formula: see text] from [Formula: see text] to [Formula: see text] except [Formula: see text], there is a fault-free cycle of length [Formula: see text] containing the edge [Formula: see text]. The result is optimal in some senses.

Velocity-Free Event-Triggered Control for Multiple Euler–Lagrange Systems With Communication Time Delays

This article considers the consensus problem of multiple Euler–Lagrange systems subject to unavailable velocity information and limited communication resources. First, a new event-triggered communication scheme, based on designing a virtual system for each agent, is proposed to achieve the coordination task without velocity information. The event-triggering conditions only rely on the states of virtual systems and allow agents to transmit virtual states only at some discrete time instants. Under the proposed strategy, the control objective can be accomplished with lower communication and measurement costs. Second, we consider the time delay effects in each information channel with event-triggered communication. For the proposed event-triggered scheme, the Zeno triggering is excluded. Finally, two simulation examples with six two-linked robot manipulator arms are given to demonstrate the effectiveness of the proposed scheme.

Private and rateless adaptive coded matrix-vector multiplication

Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm for distributed matrix-vector multiplication by taking into account (1) the privacy requirements of IoT applications and devices, and (2) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations and implementations on Android-based smartphones.

Matching-Theory-Based Low-Latency Scheme for Multitask Federated Learning in MEC Networks

Nowadays, there is an ever-increasing interests in federated learning, which allows end devices to collaboratively train a global machine learning model in a decentralized paradigm without sharing individual data. Despite the advantages of low communication cost and preserving data privacy, federated learning is also facing with new challenges to address. Practically, end devices will consider the resources cost and willingness caused by machine learning model training when they are invited to participate a federated learning task. So, how to assign the preferable tasks to the devices with high willingness has to be considered. Besides, the end devices have the property of high mobility, which means the time of devices localizing within the network is limited. Therefore, to reduce the task execution time is necessary. To address these problems, we first analyze and formulate the latency minimization problem for multitask federated learning in a multiaccess edge computing (MEC) network scenario. Then, we model the corresponding problem as a matching game to find the optimal task assignment solutions. Moreover, considering the large-scale Internet-of-Things (IoT) scenario, it is almost impossible for two sides to know the details of every individual of the other side so that the complete preference list (CPL) cannot be built in reality. Therefore, we propose an algorithm for large-scale matching with the incomplete preference list to address the problem. Finally, we conduct the numerical simulation in various cases to demonstrate the effectiveness of our proposed method. The results show that our approach can achieve similar performance with the CPL case.

Keep Your Data Locally: Federated-Learning-Based Data Privacy Preservation in Edge Computing

Recently, edge computing has attracted significant interest due to its ability to extend cloud computing utilities and services to the network edge with low response times and communication costs. In general, edge computing requires mobile users to upload their raw data to a centralized data server for further processing. However, these data usually contain sensitive information about mobile users that the users do not want to reveal, such as sexual orientation, political stance, health status, and service access history. The transmission of user data increases the leakage risk of data privacy since many extra devices can get access to these data. In this article, we attempt to keep the data of edge devices and end users on their local storage to resist the leakage of user privacy. To this end, we integrate federated learning and edge computing to propose P2FEC, a privacy-preserving framework that can construct a unified deep learning model across multiple users or devices without uploading their data to a centralized server. Furthermore, we use membership inference attacks as a case study for the privacy analysis of edge computing. The experiments show that the model constructed by our framework can achieve similar prediction performance and stricter protection of data privacy, compared to the model trained by standard edge computing.

A threshold hybrid encryption method for integrity audit without trusted center

Cloud storage with sharing services is increasingly popular among data owners. However, it is difficult for the users to know if the cloud server providers (CSPs) indeed protect their data. To verify data integrity and preserve data and key privacy in the group, this paper proposes a new threshold hybrid encryption for integrity auditing method without trusted center. The proposed method is developed based on the Advanced Encryption Standard (AES) and the Elliptic Curve Cryptography (ECC) with Shamir secret sharing. In this way, the key can be distributed and managed without trusted center, preserving the privacy of the key of the AES and users’ private key. Besides, we design and implement a novel integrity auditing and re-signature method which verifies the data integrity and solves the collusion question of the cloud and the revoked users. Security analysis and performance evaluation demonstrate that the proposed scheme realizes the correctness, security, and efficiency with a low communication and computation cost.

Ruinous Competition in News, the Postal Internet, and the Three Laws of Techno-Legal Change

Policymakers’ current approach to the problem of online misinformation, which revolves around defining the circumstances under which content platforms like Twitter and Facebook may be held liable for the speech of their users, fails to get at the root cause of the problem: the low cost of communication. The theory of monopolistic competition teaches that businesses respond to low-cost entry into their markets, and the cutthroat competition it creates, by differentiating their products, sometimes in legitimate ways, but sometimes through deceit. Misinformation on the Internet has the same source: speakers using deceit to compete for attention in a highly competitive speech market. The solution, as in all cases of ruinous competition, is to replace the falling technological barriers to market entry that have given rise to the excessive competition with new legal barriers to entry. The way to create legal barriers to online speech is not to license speech, an approach that would violate the First Amendment, but rather to treat the Internet like a brick-and-mortar postal system. In the United States, the Postal Service enjoys a “letter-box monopoly:” the exclusive right to place mail in mailboxes. This exclusivity gives the Postal Service the power to charge a price—postage—for each communication delivered to an American mailbox. The U.S. Congress—or the Postal Service itself through reinterpretation of existing law—should give the Postal Service a letter-box monopoly on social media posting: the exclusive right to charge a fee for every Tweet, Facebook post, or other social media missive delivered in America. That would greatly increase the cost of being heard on the Internet, for as the number of a poster’s followers increases, the total cost of social media “postage” would increase as well, reducing competition and the incentive to misinform. Because the letter-box monopoly has survived constitutional scrutiny for two centuries, this approach would necessarily survive First Amendment scrutiny as well.

Adaptive and Heterogeneity-Aware Coded Cooperative Computation at the Edge

Cooperative computation is a promising approach for localized data processing at the edge, e.g., for Internet of Things(IoT). Cooperative computation advocates that computationally intensive tasks in a device could be divided into sub-tasks, and offloaded to other devices or servers in close proximity. However, exploiting the potential of cooperative computation is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a coded cooperative computation framework, which we name Coded Cooperative Computation Protocol (C3P), by taking into account the heterogeneous and time-varying resources of edge devices. C3P dynamically offloads coded sub-tasks to helpers and is adaptive to time-varying resources. We show that (i) task completion delay of C3P is very close to optimal coded cooperative computation solutions, (ii) the efficiency of C3P in terms of resource utilization is higher than 99%, and (iii) C3P improves task completion delay significantly as compared to baselines via both simulations and in a testbed consisting of real Android-based smartphones.