如今，信息技术的发展正在迅速增长。在大数据时代，个人信息的隐私更加明显。主要的挑战是找到一种方法来确保在发布和分析数据时不会披露敏感的个人信息。在信任的第三方数据策展人的假设上建立了集中式差异隐私。但是，这个假设在现实中并不总是正确的。作为一种新的隐私保护模型，当地的差异隐私具有相对强大的隐私保证。尽管联邦学习相对是一种用于分布式学习的隐私方法，但它仍然引入了各种隐私问题。为了避免隐私威胁并降低沟通成本，我们建议将联合学习和当地差异隐私与动量梯度下降整合在一起，以提高机器学习模型的性能。

Federated learning seeks to address the issue of isolated data islands by making clients disclose only their local training models. However, it was demonstrated that private information could still be inferred by analyzing local model parameters, such as deep neural network model weights. Recently, differential privacy has been applied to federated learning to protect data privacy, but the noise added may degrade the learning performance much. Typically, in previous work, training parameters were clipped equally and noises were added uniformly. The heterogeneity and convergence of training parameters were simply not considered. In this paper, we propose a differentially private scheme for federated learning with adaptive noise (Adap DP-FL). Specifically, due to the gradient heterogeneity, we conduct adaptive gradient clipping for different clients and different rounds; due to the gradient convergence, we add decreasing noises accordingly. Extensive experiments on real-world datasets demonstrate that our Adap DP-FL outperforms previous methods significantly.

联合学习（FL）是一个系统，中央聚合器协调多个客户解决机器学习问题的努力。此设置允许分散培训数据以保护隐私。本文的目的是提供针对医疗保健的FL系统的概述。 FL在此根据其框架，架构和应用程序进行评估。这里显示的是，FL通过中央聚合器服务器通过共享的全球深度学习（DL）模型解决了前面的问题。本文研究了最新的发展，并提供了来自FL研究的快速增长的启发，列出了未解决的问题。在FL的背景下，描述了几种隐私方法，包括安全的多方计算，同态加密，差异隐私和随机梯度下降。此外，还提供了对各种FL类的综述，例如水平和垂直FL以及联合转移学习。 FL在无线通信，服务建议，智能医学诊断系统和医疗保健方面有应用，本文将在本文中进行讨论。我们还对现有的FL挑战进行了彻底的审查，例如隐私保护，沟通成本，系统异质性和不可靠的模型上传，然后是未来的研究指示。

通信技术和互联网的最新进展与人工智能（AI）启用了智能医疗保健。传统上，由于现代医疗保健网络的高性性和日益增长的数据隐私问题，AI技术需要集中式数据收集和处理，这可能在现实的医疗环境中可能是不可行的。作为一个新兴的分布式协作AI范例，通过协调多个客户（例如，医院）来执行AI培训而不共享原始数据，对智能医疗保健特别有吸引力。因此，我们对智能医疗保健的使用提供了全面的调查。首先，我们在智能医疗保健中展示了近期进程，动机和使用FL的要求。然后讨论了近期智能医疗保健的FL设计，从资源感知FL，安全和隐私感知到激励FL和个性化FL。随后，我们对关键医疗领域的FL新兴应用提供了最先进的综述，包括健康数据管理，远程健康监测，医学成像和Covid-19检测。分析了几个最近基于智能医疗保健项目，并突出了从调查中学到的关键经验教训。最后，我们讨论了智能医疗保健未来研究的有趣研究挑战和可能的指示。

联合学习（FL）和分裂学习（SL）是两种新兴的协作学习方法，可能会极大地促进物联网（IoT）中无处不在的智能。联合学习使机器学习（ML）模型在本地培训的模型使用私人数据汇总为全球模型。分裂学习使ML模型的不同部分可以在学习框架中对不同工人进行协作培训。联合学习和分裂学习，每个学习都有独特的优势和各自的局限性，可能会相互补充，在物联网中无处不在的智能。因此，联合学习和分裂学习的结合最近成为一个活跃的研究领域，引起了广泛的兴趣。在本文中，我们回顾了联合学习和拆分学习方面的最新发展，并介绍了有关最先进技术的调查，该技术用于将这两种学习方法组合在基于边缘计算的物联网环境中。我们还确定了一些开放问题，并讨论了该领域未来研究的可能方向，希望进一步引起研究界对这个新兴领域的兴趣。

使用人工智能（AI）赋予无线网络中数据量的前所未有的数据量激增，为提供无处不在的数据驱动智能服务而开辟了新的视野。通过集中收集数据集和培训模型来实现传统的云彩中心学习（ML）基础的服务。然而，这种传统的训练技术包括两个挑战：（i）由于数据通信增加而导致的高通信和能源成本，（ii）通过允许不受信任的各方利用这些信息来威胁数据隐私。最近，鉴于这些限制，一种新兴的新兴技术，包括联合学习（FL），以使ML带到无线网络的边缘。通过以分布式方式培训全局模型，可以通过FL Server策划的全局模型来提取数据孤岛的好处。 FL利用分散的数据集和参与客户的计算资源，在不影响数据隐私的情况下开发广义ML模型。在本文中，我们介绍了对FL的基本面和能够实现技术的全面调查。此外，提出了一个广泛的研究，详细说明了无线网络中的流体的各种应用，并突出了他们的挑战和局限性。进一步探索了FL的疗效，其新兴的前瞻性超出了第五代（B5G）和第六代（6G）通信系统。本调查的目的是在关键的无线技术中概述了流动的技术，这些技术将作为建立对该主题的坚定了解的基础。最后，我们向未来的研究方向提供前进的道路。

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients' private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to parameters at the clients' side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noises. Then we develop a theoretical convergence bound of the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between a convergence performance and privacy protection levels, i.e., better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number N of overall clients participating in FL can improve the convergence performance; and 3) There is an optimal number aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a K-client random scheduling strategy, where K (1 ≤ K < N ) clients are randomly selected from the N overall clients to participate in each aggregation. We also develop a corresponding convergence bound for the loss function in this case and the K-client random scheduling strategy also retains the above three properties. Moreover, we find that there is an optimal K that achieves the best convergence performance at a

Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy.

通常利用机器学习方法并有效地将智能电表读数从家庭级别分解为设备级消耗，可以帮助分析用户的电力消耗行为并启用实用智能能源和智能网格申请。最近的研究提出了许多基于联邦深度学习（FL）的新型NILM框架。但是，缺乏综合研究，探讨了不同基于FL的NILM应用程序方案中的实用性优化方案和隐私保护方案。在本文中，我们首次尝试通过开发分布式和隐私的尼尔姆（DP2-NILM）框架来进行基于FL的NILM，重点关注实用程序优化和隐私保护，并在实用的NILM场景上进行比较实验基于现实世界的智能电表数据集。具体而言，在实用程序优化方案（即FedAvg和FedProx）中检查了两种替代联合学习策略。此外，DP2-NILM提供了不同级别的隐私保证，即联合学习的当地差异隐私学习和联合的全球差异隐私学习。在三个现实世界数据集上进行了广泛的比较实验，以评估所提出的框架。

联邦学习一直是一个热门的研究主题，使不同组织的机器学习模型的协作培训在隐私限制下。随着研究人员试图支持更多具有不同隐私方法的机器学习模型，需要开发系统和基础设施，以便于开发各种联合学习算法。类似于Pytorch和Tensorflow等深度学习系统，可以增强深度学习的发展，联邦学习系统（FLSS）是等效的，并且面临各个方面的面临挑战，如有效性，效率和隐私。在本调查中，我们对联合学习系统进行了全面的审查。为实现流畅的流动和引导未来的研究，我们介绍了联合学习系统的定义并分析了系统组件。此外，我们根据六种不同方面提供联合学习系统的全面分类，包括数据分布，机器学习模型，隐私机制，通信架构，联合集市和联合的动机。分类可以帮助设计联合学习系统，如我们的案例研究所示。通过系统地总结现有联合学习系统，我们展示了设计因素，案例研究和未来的研究机会。

随着物联网，AI和ML/DL算法的出现，数据驱动的医疗应用已成为一种有前途的工具，用于从医学数据设计可靠且可扩展的诊断和预后模型。近年来，这引起了从学术界到工业的广泛关注。这无疑改善了医疗保健提供的质量。但是，由于这些基于AI的医疗应用程序在满足严格的安全性，隐私和服务标准（例如低延迟）方面的困难，因此仍然采用较差。此外，医疗数据通常是分散的和私人的，这使得在人群之间产生强大的结果具有挑战性。联邦学习（FL）的最新发展使得以分布式方式训练复杂的机器学习模型成为可能。因此，FL已成为一个积极的研究领域，尤其是以分散的方式处理网络边缘的医疗数据，以保护隐私和安全问题。为此，本次调查论文重点介绍了数据共享是重大负担的医疗应用中FL技术的当前和未来。它还审查并讨论了当前的研究趋势及其设计可靠和可扩展模型的结果。我们概述了FL将军的统计问题，设备挑战，安全性，隐私问题及其在医疗领域的潜力。此外，我们的研究还集中在医疗应用上，我们重点介绍了全球癌症的负担以及有效利用FL来开发计算机辅助诊断工具来解决这些诊断工具。我们希望这篇评论是一个检查站，以彻底的方式阐明现有的最新最新作品，并为该领域提供开放的问题和未来的研究指示。

Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among data owners, FL has gained significant attention of organizations. The idea of FL is to enable collaborating participants train machine learning (ML) models on decentralized data without breaching privacy. In simpler words, federated learning is the approach of ``bringing the model to the data, instead of bringing the data to the mode''. Federated learning, when applied to data which is partitioned vertically across participants, is able to build a complete ML model by combining local models trained only using the data with distinct features at the local sites. This architecture of FL is referred to as vertical federated learning (VFL), which differs from the conventional FL on horizontally partitioned data. As VFL is different from conventional FL, it comes with its own issues and challenges. In this paper, we present a structured literature review discussing the state-of-the-art approaches in VFL. Additionally, the literature review highlights the existing solutions to challenges in VFL and provides potential research directions in this domain.

智能仪表测量值虽然对于准确的需求预测至关重要，但仍面临一些缺点，包括消费者的隐私，数据泄露问题，仅举几例。最近的文献探索了联合学习（FL）作为一种有前途的隐私机器学习替代方案，该替代方案可以协作学习模型，而无需将私人原始数据暴露于短期负载预测中。尽管有着美德，但标准FL仍然容易受到棘手的网络威胁，称为拜占庭式攻击，这是由错误和/或恶意客户进行的。因此，为了提高联邦联邦短期负载预测对拜占庭威胁的鲁棒性，我们开发了一个最先进的基于私人安全的FL框架，以确保单个智能电表的数据的隐私，同时保护FL的安全性模型和架构。我们提出的框架利用了通过符号随机梯度下降（SignsGD）算法的梯度量化的想法，在本地模型培训后，客户仅将梯度的“符号”传输到控制中心。当我们通过涉及一组拜占庭攻击模型的基准神经网络的实验突出显示时，我们提出的方法会非常有效地减轻此类威胁，从而优于常规的FED-SGD模型。

Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models only collecting the intermediate parameters instead of the real user data, which greatly enhances the user privacy. Beside, federated recommendation systems enable to collaborate with other data platforms to improve recommended model performance while meeting the regulation and privacy constraints. However, federated recommendation systems faces many new challenges such as privacy, security, heterogeneity and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this survey, we-(1) summarize some common privacy mechanisms used in federated recommendation systems and discuss the advantages and limitations of each mechanism; (2) review some robust aggregation strategies and several novel attacks against security; (3) summarize some approaches to address heterogeneity and communication costs problems; (4)introduce some open source platforms that can be used to build federated recommendation systems; (5) present some prospective research directions in the future. This survey can guide researchers and practitioners understand the research progress in these areas.

联邦机器学习利用边缘计算来开发网络用户数据的模型，但联合学习的隐私仍然是一个重大挑战。已经提出了使用差异隐私的技术来解决这一点，但是带来了自己的挑战 - 许多人需要一个值得信赖的第三方，或者增加了太多的噪音来生产有用的模型。使用多方计算的\ EMPH {SERVE聚合}的最新进步消除了对第三方的需求，但是在计算上尤其在规模上昂贵。我们提出了一种新的联合学习协议，利用了一种基于与错误学习的技术的新颖差异私有的恶意安全聚合协议。我们的协议优于当前最先进的技术，并且经验结果表明它缩放到大量方面，具有任何差别私有联合学习方案的最佳精度。

为了满足下一代无线通信网络的极其异构要求，研究界越来越依赖于使用机器学习解决方案进行实时决策和无线电资源管理。传统的机器学习采用完全集中的架构，其中整个培训数据在一个节点上收集，即云服务器，显着提高了通信开销，并提高了严重的隐私问题。迄今为止，最近提出了作为联合学习（FL）称为联合学习的分布式机器学习范式。在FL中，每个参与边缘设备通过使用自己的培训数据列举其本地模型。然后，通过无线信道，本地训练模型的权重或参数被发送到中央ps，聚合它们并更新全局模型。一方面，FL对优化无线通信网络的资源起着重要作用，另一方面，无线通信对于FL至关重要。因此，FL和无线通信之间存在“双向”关系。虽然FL是一个新兴的概念，但许多出版物已经在FL的领域发表了发布及其对下一代无线网络的应用。尽管如此，我们注意到没有任何作品突出了FL和无线通信之间的双向关系。因此，本调查纸的目的是通过提供关于FL和无线通信之间的相互依存性的及时和全面的讨论来弥合文学中的这种差距。

联邦学习（FL）和分裂学习（SL）是两个流行的分布式机器学习方法。遵循模型到数据方案;客户培训和测试机器学习模型而不共享原始数据。由于客户端和服务器之间的机器学习模型架构，SL提供比FL更好的模型隐私。此外，分割模型使SL成为资源受限环境的更好选择。然而，由于基于中继的训练，SL表现在多个客户端的继电器训练引起的速度。在这方面，本文提出了一种名为Splitfed Learning（SFL）的新方法，该方法可分摊两种方法消除其固有缺点，以及包含差异隐私和PIXELD的精制架构配置，以增强数据隐私和模型鲁棒性。我们的分析和经验结果表明，（纯）SFL提供了类似的测试精度和通信效率，作为SL，同时每个全球时代显着降低其用于多个客户端的SL中的计算时间。此外，如SL在SL中，它的通信效率随着客户的数量而改善。此外，在扩展实验环境下进一步评估了具有隐私和鲁棒性度量的SFL的性能。

Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively small amounts of data each. In this paper, we present an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs. Combining differential privacy with secure multiparty computation enables us to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust. Our system is therefore a scalable approach that protects against inference threats and produces models with high accuracy. Additionally, our system can be used to train a variety of machine learning models, which we validate with experimental results on 3 different machine learning algorithms. Our experiments demonstrate that our approach out-performs state of the art solutions. CCS CONCEPTS• Security and privacy → Privacy-preserving protocols; Trust frameworks; • Computing methodologies → Learning settings.

联合学习（FL）已成为协作分布式学习的隐私解决方案，客户直接在其设备上训练AI模型，而不是与集中式（潜在的对手）服务器共享数据。尽管FL在某种程度上保留了本地数据隐私，但已显示有关客户数据的信息仍然可以从模型更新中推断出来。近年来，已经制定了各种隐私计划来解决这种隐私泄漏。但是，它们通常以牺牲模型性能或系统效率为代价提供隐私，而在实施FL计划时，平衡这些权衡是一个至关重要的挑战。在本手稿中，我们提出了一个保护隐私的联合学习（PPFL）框架，该框架建立在控制理论中的矩阵加密和系统沉浸工具的协同作用上。这个想法是将学习算法（随机梯度体面（SGD））浸入更高维度的系统（所谓的目标系统）中，并设计目标系统的动力学，以便：浸入原始SGD的轨迹： /嵌入其轨迹中，并在加密数据上学习（在这里我们使用随机矩阵加密）。矩阵加密是在服务器上重新重新格式化的，作为将原始参数映射到更高维的参数空间的坐标的随机更改，并强制执行目标SGD收敛到原始SGD Optiral解决方案的加密版本。服务器使用浸入式地图的左侧逆汇总模型解密。我们表明，我们的算法提供与标准FL相同的准确性和收敛速度，而计算成本可忽略不计，同时却没有透露有关客户数据的信息。