Towards efficient communications in federated learning: A contemporary survey

In the traditional distributed machine learning scenario, the user’s private data is transmitted between clients and a central server, which results in significant potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a particular distributed machine learning procedure with privacy protection mechanisms, which can achieve multi-party collaborative computing without revealing the original data. However, in practice, FL faces a variety of challenging communication problems. This review seeks to elucidate the relationship between these communication issues by methodically assessing the development of FL communication research from three perspectives: communication efficiency, communication environment, and communication resource allocation. Firstly, we sort out the current challenges existing in the communications of FL. Second, we have collated FL communications-related papers and described the overall development trend of the field based on their logical relationship. Ultimately, we discuss the future directions of research for communications in FL.     

On the feasibility of Federated Learning towards on-demand client deployment at the edge

Nowadays, researchers are investing their time and devoting their efforts in developing and motivating the 6G vision and resources that are not available in 5G. Edge computing and autonomous vehicular driving applications are more enhanced under the 6G services that are provided to successfully operate tasks. The huge volume of data resulting from such applications can be a plus in the AI and Machine Learning (ML) world. Traditional ML models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated Learning (FL) plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients in real-time wherever and whenever needed. In fact, some mobile and vehicular devices are not available to serve as clients in the FL due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in FL offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using any type of client devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the deployment of clients whenever and wherever needed.     

边缘计算在图书馆智慧服务中的应用探索

[目的/意义]万物互联时代背景下,集中式的云计算模型将难以适应智慧图书馆的海量数据处理需求,而边缘计算的出现为这一问题的解决提供了新的技术手段,对图书馆的智慧服务产生影响。[方法/过程]首先阐释了边缘计算的基本架构与特点,然后在探讨图书馆智慧服务需求基础上,分析了图书馆智慧服务融入边缘计算的优势及可能性,最后构建了基于边缘计算的图书馆智慧服务体系框架,并对框架的每一层级进行了功能分析。[结果/结论]该体系框架可提高图书馆智慧服务响应实时性,降低智慧图书馆数据处理成本和网络带宽压力,开辟了促进图书馆智慧服务能力提升的新思路。     

Data information processing of traffic digital twins in smart cities using edge intelligent federation learning

The present work analyzes the application of deep learning in the context of digital twins (DTs) to promote the development of smart cities. According to the theoretical basis of DTs and the smart city construction, the five-dimensional DTs model is discussed to propose the conceptual framework of the DTs city. Then, edge computing technology is introduced to build an intelligent traffic perception system based on edge computing combined with DTs. Moreover, to improve the traffic scene recognition accuracy, the Single Shot MultiBox Detector (SSD) algorithm is optimized by the residual network, form the SSD-ResNet50 algorithm, and the DarkNet-53 is also improved. Finally, experiments are conducted to verify the effects of the improved algorithms and the data enhancement method. The experimental results indicate that the SSD-ResNet50 and the improved DarkNet-53 algorithm show fast training speed, high recognition accuracy, and favorable training effect. Compared with the original algorithms, the recognition time of the SSD-ResNet50 algorithm and the improved DarkNet-53 algorithm is reduced by 6.37ms and 4.25ms, respectively. The data enhancement method used in the present work is not only suitable for the algorithms reported here, but also has a good influence on other deep learning algorithms. Moreover, SSD-ResNet50 and improved DarkNet-53 algorithms have significant applicable advantages in the research of traffic sign target recognition. The rigorous research with appropriate methods and comprehensive results can offer effective reference for subsequent research on DTs cities.     

[2015年第2期] 云计算的价值创造及其机理

云计算在各行业的创新应用 , 衍生出诸多新型业态和新型商业模式, 产生了巨大的 经济价值和社会价值,主要体现在： 云计算集中了信息技术资源和服务,大幅提高了 IT资源的 应用效率; 降低了应用技术门槛, 最大限度扩展用户规模; 集中整合了数据资源, 挖掘出大数 据价值潜力; 引起体制机制的变革, 以技术方式突破体制屏障; 加强了信息系统综合集成, 实 现智慧管理与服务。 云计算应用创新具有独特的价值生成机理。 首先, 云计算通过将市场交 易双方的博弈改变为无限重复动态博弈, 形成诚信自律机制, 提高了市场交易效率从而形成 增值; 其次,云计算的创新应用向资本市场展示了收益前景,以内部学习效应形成了以未来收 益弥补当前亏损的资本补偿机制 , 挖掘了知识价值; 第三, 云计算形成了新型互联网商业平 台 ,以交叉补贴机制确保长期可持续经营,创造了低成本、高收益的价值增值模式。 鉴于云计 算平台已经成为涉及公共利益、承载公共服务的信息基础设施, 建议将云设施视为信息基础 设施,纳入监管保护体系; 制定针对云计算的知识产权和个人信息保护措施,鼓励云计算创新 应用 ;开展多层次试点示范,帮助中小企业和社会大众利用云计算创新创业。     

Mobile edge computing based QoS optimization in medical healthcare applications

Emerging trends in mobile edge computing for developing the efficient healthcare application such as, remote monitoring of the patients with central electronics clouds (e-Clouds) and their increasing voluminous multimedia have caught the attention of everyone in industry and academia. So, clear visualization, big sensing level, and better quality of service (QoS) is the foremost priority. This paper proposes the window-based Rate Control Algorithm (w-RCA) to optimize the medical quality of service (m-QoS) in the mobile edge computing based healthcare by considering the network parameters for instance, peak-to-mean ratio (PMR), standard deviation (Std.dev), delay and jitter during 8 min medical video stream named “Navigation to the Uterine Horn, transection of the horn and re-anastomosis’ transmission over 5 G networks. The performance of the proposed w-RCA is evaluated and compared with the conventional battery smoothing algorithm (BSA) and Baseline by using MPEG-4 encoder for optimizing m-QoS at the source or the server side. The experimental results demonstrate that the w-RCA outperforms the BSA and Baseline by optimizing QoS in remote healthcare application i.e., Tele-surgery. Besides, it is observed and analyzed that w-RCA produces better and effective results at small buffer and window sizes unlike BSA and Baseline by adopting large buffer size during QoS optimization.     

基于蜻蜓算法和最小二乘向量机的小批量生产质量预测

采用蜻蜓算法(DA)和最小二乘支持向量机(LSSVM)的方法,解决生产过程中小批量产品在质量预测方面的问题。首先以汽车变速箱轴承内圈孔直径的尺寸作为预测数据,连续观测12个单位时间,并记录每个单位时间轴承内圈孔直径的尺寸数据,进行归一化处理;其次采用LSSVM对变速箱轴承内圈孔直径加工过程变化进行量化分析,并采用蜻蜓算法优化LSSVM参数;最后将DA-LSSVM综合方法与多种预测模型进行对比分析。结果表明,DA-LSSVM方法可以提高预测模型的训练预测精度,缩短训练时间。     

Fast-DRD: Fast decentralized reinforcement distillation for deadline-aware edge computing

Edge computing has recently gained momentum as it provides computing services for mobile devices through high-speed networks. In edge computing system optimization, deep reinforcement learning(DRL) enhances the quality of services(QoS) and shorts the age of information(AoI). However, loosely coupled edge servers saturate a noisy data space for DRL exploration, and learning a reasonable solution is enormously costly. Most existing works assume that the edge is an exact observation system and harvests well-labeled data for the pretraining of DRL neural networks. However, this assumption stands in opposition to the motivation of driving DRL to explore unknown information and increases the scheduling and computing costs in large-scale dynamic systems. This article leverages DRL with a distillation module to drive learning efficiency for edge computing with partial observation. We formulate the deadline-aware offloading problem as a decentralized partially observable Markov decision process (Dec-POMDP) with distillation, called fast decentralized reinforcement distillation(Fast-DRD). Each edge server decides makes offloading decisions in accordance with its own observations and learning strategies in a decentralized manner. By defining trajectory observation history(TOH) distillation and trust distillation to avoid overfitting, Fast-DRD learns a suitable offloading model in a noisy partially observed edge system and reduces the cost for communication among servers. Finally, experimental simulations are presented to evaluate and compare the effectiveness and complexity of Fast-DRD.     

对一种无证书部分盲签名方案的密码学分析

指出了一种新的无证书部分盲签名机制存在公钥替换攻击,分析了形成攻击的原因,并且通过修改签名验证算法改进了该部分盲签名机制。分析表明,这一改进方案有效地防止了其存在的公钥替换攻击。     

Making use of cloud computing for healthcare provision: Opportunities and challenges

Cloud or utility computing is an emerging new computing paradigm designed to deliver numerous computing services through networked media such as the Web. This approach offers several advantages to potential users such as “metered” use (i.e., pay-as-you-go) which offers scalability, online delivery of software and virtual hardware services (e.g., collaboration programmes, virtual servers, virtual storage devices) which would enable organizations to obviate the need to own, maintain and update their software and hardware infrastructures. The flexibility of this emerging computing service has opened many possibilities for organizations that did not exist before. Among those organizations are those engaged in healthcare provision. The aim of this article is to shed some light on this development and explore the potential (and future) of cloud computing in contributing to the advancement of healthcare provision. A small case study will also be presented and discussed.     

细胞计算进展与展望

细胞是生物体最基本的结构和功能单元,它蕴含了大自然几千万年进化所沉淀的智能。细胞外膜将细胞内部和细胞环境分割开,控制细胞内外物质的进出,而细胞内膜将细胞内部分成具有不同生物功能的细胞器,这使得细胞自身构成了一个分布式并行信息处理系统。文章介绍了研究细胞计算的背景,细胞的基本结构和功能,以及基于细胞的结构和功能而发展起来的计算机科学新领域:膜计算。膜计算目前主要有3类计算模型:细胞型计算模型、组织型计算模型和神经型计算模型,这3类计算模型分别以单个细胞、细胞群体和神经元作为计算载体。膜计算在生物系统建模等方面具有重要的应用价值。随着生物技术的发展,人们用大肠杆菌等实现了部分膜计算模型。最后,展望了膜计算在生物学、医学、大规模数据存储、大规模并行计算等方面的应用前景。     

跨组织医疗健康大数据聚合与案例知识推理方法研究

【目的/意义】医疗健康大数据为智慧医疗提供了前所未有的机遇。然而“数据烟囱”“信息孤岛”和低效的知识服务方法严重阻碍医疗健康服务模式创新。如何通过医疗健康大数据深度聚合和动态知识服务,实现面向全方位全周期智慧医疗服务的知识管理创新成为当前医疗信息资源管理领域的重要问题。【方法/过程】介绍了一种面向大规模多源异构医疗健康数据安全共享的联邦学习机制和深度聚合方法,提出了人机协同的医疗案例库构建方法和基于杰卡德距离算法的医疗案例知识推理方法。【结果/结论】该方法为智慧诊疗、临床教学和辅助科研提供了一体化知识管理服务框架。【创新/局限】该方法不仅为智慧医疗与精准健康管理提供了一种数据管理方法体系,还为5P智慧医疗服务新模式构建提供了新的思路。     

一种基于区块链与智能合约的科学数据安全溯源方法

[目的/意义] 数据是开展科学研究活动的基础,确保数据的质量、防止数据被篡改已成为影响研究结果的重要因素,为了避免数据造假、数据虚报、伪造结果以符合关键科学研究目标等数据欺诈行为,必须保持数据的溯源。[方法/过程] 将溯源系统的安全威胁分为内部因素和外部因素,基于智能合约和开放溯源模型（OPM）,通过投票过程实现溯源信息准入机制,利用区块链的分布式特性和不可改变性,提出一种可靠的数据溯源收集、验证和管理区块链系统模型。[结果/结论] 在科学数据创建、分发、流通、使用过程中可以有效、安全地捕获和验证溯源记录,防止溯源信息被恶意更改的同时保护了隐私信息,为确保科研数据的客观真实提供思路和方法参考。     

Seeing is believing: Towards interactive visual exploration of data privacy in federated learning

Federated learning (FL), as a popular distributed machine learning paradigm, has driven the integration of knowledge in ubiquitous data owners under one roof. Although designed for privacy-preservation by nature, the supposed well-sanitized parameters still convey sensitive information (e.g., reconstruction attack), while existing technical countermeasures provide weak explainability for privacy understanding and protection practices of general users. This work investigates these privacy concerns with an exploratory study and elaborates on data owners’ expectations in FL. Based on the analysis, we design the first interactive visualization system for FL privacy that supports intelligible privacy inspection and adjustment for data owners. Specifically, our proposal facilitates sample recommendation for joint privacy–performance training at cold start. Then it provides visual interpretation and attention rendering of privacy risks in view of multiple attacking channels and a holistic view. Further it supports interactive privacy enhancement involving both user initiative and differential privacy technique, and iterative trade-off with real-time inference accuracy estimation. We evaluate the effectiveness of the system and collect qualitative feedbacks from users. The results demonstrate that 96.7% of users acknowledge the benefits to privacy inspection and adjustment and 90.3% are willing to use our system. More importantly, 87.1% increase the willingness of contributing data for FL.     

一种新型信息基础设施：高通量低熵算力网（信息高铁）

人类正在进入一个“人机物”三元融合的万物智能互联时代,需要一种新型信息基础设施,即全球规模的高通量低熵算力网,形象地简称为“信息高铁”。文章介绍了信息高铁的愿景,包括基础性需求、关键科学技术问题和系统结构。与互联网、云计算、大数据、物联网等现有网络计算系统相比,信息高铁的目标是原生支持“人机物”三元融合和低熵有序,降低系统无序的负面影响,提升系统通量与应用品质。     

人工智能及云计算在智慧农业教学中的应用

智慧农业伴随着人工智能、云计算、物联网、大数据等现代信息技术的发展应运而生。近年来,智慧农业在全球范围内逐渐被重视,在未来农业发展领域将扮演着极其重要的角色。本文主要通过探讨如何通过在智慧农业教学中普及人工智能与云计算方面的相关知识应用,从而推动智慧农业教学的进步与改革,让学生从根本上了解我国智慧农业发展至今其中所存在的问题和相应的解决方法,目的在于能够更快地在我国智慧农业起步阶段,提高农学学子的知识水平,在技术、应用、产业和机制方面突破瓶颈,来加速中国智慧农业的跨越式发展。     

SecureCPS: Cognitive inspired framework for detection of cyber attacks in cyber–physical systems

In the era of autonomous systems, the security is indispensable module for flexible computing environment. Due to increased computer power and network speed, a new computing paradigm, such as cognitive inspired computing, will emerge. Such a paradigm provides human-centered services that are convenient and enjoyable at any time, anywhere, and on any device. On the foundation of smart city environment, human computer interaction, intelligent services, and universal device connectivity, Cyber Physical Computing for Cyber Physical systems has recently been investigated. However, in this proposal, a cognitive inspired framework for securing CPS is scrutinized. The cognitive ability is conceded to the search engines by updating the PageRank ranking methodology. The proposed framework, named SecureCPS is trained with real time collective dataset for marking the relevancy of web page with the support the facial expressions. The eye regions are marked using Focal Point Detector algorithm. The framework is validated with machine learning models and resulted in achieving 98.51% accuracy and its outperforms the existing frameworks.     

智能电网中的云计算平台研究

统一坚强智能电网是未来电网发展的趋势,信息技术是其建设和发展的重要支撑,云计算为坚强智能电网下的海量数据处理、分析、存储、管理与计算平台提供了新的技术手段。为了更好地研究和利用云计算构建智能电网中的信息平台,首先对云计算进行了概述,总结了智能电网对信息平台的要求及云计算在智能电网中的应用,并结合已有研究对智能电网下云计算平台研究的新内容进行了讨论分析。     

边缘计算领域核心专利挖掘——基于专利引证视角

以IncoPat为数据源构建检索策略,采用专利被引频次指标对全球边缘计算领域核心专利进行挖掘,并对该核心专利进行向后引证和向前引证分析,包括被引证年份分布、专利权人分布、技术分布和自引证技术演化路线分析。研究结果表明,美国是全球边缘计算技术最主要的应用国和来源国;核心专利US20110075675A1以年均60.6次被引频次排名第一,其向前引证主体包括IBM、Cisco、华为、NEC和三星电子等,且存在创新竞争关系;边缘计算技术演化路线由早期的边缘呼叫和会话管理,逐渐演化到分组路由和无线承载标识,并最终过渡到智能路由和无线设备监控策略研究。