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Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems
IEEE Communications Surveys & Tutorials ( IF 35.6 ) Pub Date : 2021-02-10 , DOI: 10.1109/comst.2021.3058573
Omar Abdel Wahab , Azzam Mourad , Hadi Otrok , Tarik Taleb

The communication and networking field is hungry for machine learning decision-making solutions to replace the traditional model-driven approaches that proved to be not rich enough for seizing the ever-growing complexity and heterogeneity of the modern systems in the field. Traditional machine learning solutions assume the existence of (cloud-based) central entities that are in charge of processing the data. Nonetheless, the difficulty of accessing private data, together with the high cost of transmitting raw data to the central entity gave rise to a decentralized machine learning approach called Federated Learning . The main idea of federated learning is to perform an on-device collaborative training of a single machine learning model without having to share the raw training data with any third-party entity. Although few survey articles on federated learning already exist in the literature, the motivation of this survey stems from three essential observations. The first one is the lack of a fine-grained multi-level classification of the federated learning literature, where the existing surveys base their classification on only one criterion or aspect. The second observation is that the existing surveys focus only on some common challenges, but disregard other essential aspects such as reliable client selection, resource management and training service pricing. The third observation is the lack of explicit and straightforward directives for researchers to help them design future federated learning solutions that overcome the state-of-the-art research gaps. To address these points, we first provide a comprehensive tutorial on federated learning and its associated concepts, technologies and learning approaches. We then survey and highlight the applications and future directions of federated learning in the domain of communication and networking. Thereafter, we design a three-level classification scheme that first categorizes the federated learning literature based on the high-level challenge that they tackle. Then, we classify each high-level challenge into a set of specific low-level challenges to foster a better understanding of the topic. Finally, we provide, within each low-level challenge, a fine-grained classification based on the technique used to address this particular challenge. For each category of high-level challenges, we provide a set of desirable criteria and future research directions that are aimed to help the research community design innovative and efficient future solutions. To the best of our knowledge, our survey is the most comprehensive in terms of challenges and techniques it covers and the most fine-grained in terms of the multi-level classification scheme it presents.

中文翻译:

联合机器学习:通信和网络系统中的调查,多层分类,可取的标准和未来方向

通信和网络领域迫切需要机器学习决策解决方案来代替传统的模型驱动方法,这种方法被证明不够丰富,无法抓住该领域现代系统不断增长的复杂性和异构性。传统的机器学习解决方案假定存在(基于云的)中央实体,这些实体负责处理数据。尽管如此,访问私有数据的困难以及将原始数据传输到中央实体的高昂成本导致了一种分散的机器学习方法,该方法称为联合学习 。联合学习的主要思想是对单个机器学习模型执行设备上的协作训练,而不必与任何第三方实体共享原始训练数据。尽管文献中几乎没有关于联邦学习的调查文章,但该调查的动机来自三个基本观察。第一个是缺乏对联邦学习文献的细粒度分类,现有的调查仅基于一个标准或方面对分类进行了分类。第二个观察结果是,现有调查仅关注一些常见挑战,而忽略了其他重要方面,例如可靠的客户选择,资源管理和培训服务定价。第三个发现是,研究人员缺乏明确而直接的指令来帮助他们设计能够克服最新研究空白的未来联合学习解决方案。为了解决这些问题,我们首先提供有关联合学习及其相关概念,技术和学习方法的综合教程。然后,我们调查并重点介绍联合学习在通信和网络领域中的应用和未来的发展方向。此后,我们设计了一个三级分类方案,该方案首先根据联合会处理的高级挑战对联合学习文献进行分类。然后,我们将每个高级别挑战分为一组特定的低级别挑战,以促进对该主题的更好理解。最后,我们在每个低级别挑战中提供 基于用于解决此特定挑战的技术的细粒度分类。对于每种高级别挑战,我们提供了一组理想的标准和未来的研究方向,旨在帮助研究机构设计创新且高效的未来解决方案。据我们所知,就所涉及的挑战和技术而言,我们的调查是最全面的,就其提出的多级分类方案而言,我们的调查也是最细粒度的。
更新日期:2021-02-10
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