Applications of Federated Learning; Taxonomy, Challenges, and Research Trends

【Author】 Shaheen, Momina; Farooq, Muhammad Shoaib; Umer, Tariq; Kim, Byung-Seo

【Source】ELECTRONICS

【Abstract】The federated learning technique (FL) supports the collaborative training of machine learning and deep learning models for edge network optimization. Although a complex edge network with heterogeneous devices having different constraints can affect its performance, this leads to a problem in this area. Therefore, some research can be seen to design new frameworks and approaches to improve federated learning processes. The purpose of this study is to provide an overview of the FL technique and its applicability in different domains. The key focus of the paper is to produce a systematic literature review of recent research studies that clearly describes the adoption of FL in edge networks. The search procedure was performed from April 2020 to May 2021 with a total initial number of papers being 7546 published in the duration of 2016 to 2020. The systematic literature synthesizes and compares the algorithms, models, and frameworks of federated learning. Additionally, we have presented the scope of FL applications in different industries and domains. It has been revealed after careful investigation of studies that 25% of the studies used FL in IoT and edge-based applications and 30% of studies implement the FL concept in the health industry, 10% for NLP, 10% for autonomous vehicles, 10% for mobile services, 10% for recommender systems, and 5% for FinTech. A taxonomy is also proposed on implementing FL for edge networks in different domains. Moreover, another novelty of this paper is that datasets used for the implementation of FL are discussed in detail to provide the researchers an overview of the distributed datasets, which can be used for employing FL techniques. Lastly, this study discusses the current challenges of implementing the FL technique. We have found that the areas of medical AI, IoT, edge systems, and the autonomous industry can adapt the FL in many of its sub-domains; however, the challenges these domains can encounter are statistical heterogeneity, system heterogeneity, data imbalance, resource allocation, and privacy.

【Keywords】federated learning; edge devices; edge computing; IoT

【标题】联邦学习的应用；分类学、挑战和研究趋势

【摘要】联邦学习技术 (FL) 支持机器学习和深度学习模型的协作训练，以进行边缘网络优化。尽管具有不同约束的异构设备的复杂边缘网络会影响其性能，但这会导致该领域的问题。因此，可以看到一些研究设计新的框架和方法来改进联邦学习过程。本研究的目的是概述 FL 技术及其在不同领域的适用性。本文的重点是对最近的研究进行系统的文献回顾，清楚地描述边缘网络中 FL 的采用。搜索过程从 2020 年 4 月到 2021 年 5 月进行，在 2016 年到 2020 年期间，最初发表的论文总数为 7546 篇。系统文献综合和比较了联邦学习的算法、模型和框架。此外，我们还介绍了 FL 在不同行业和领域的应用范围。仔细调查研究后发现，25% 的研究在物联网和基于边缘的应用中使用了 FL，30% 的研究在健康行业实施了 FL 概念，10% 用于 NLP，10% 用于自动驾驶汽车，10 % 用于移动服务，10% 用于推荐系统，5% 用于金融科技。还提出了一种分类法，用于在不同域中为边缘网络实现 FL。此外，本文的另一个新颖之处在于详细讨论了用于实现 FL 的数据集，以便为研究人员提供分布式数据集的概述，这些数据集可用于采用 FL 技术。最后，本研究讨论了当前实施 FL 技术的挑战。我们发现医疗人工智能、物联网、边缘系统和自主产业领域可以在其许多子领域中适应 FL；然而，这些领域可能遇到的挑战是统计异质性、系统异质性、数据不平衡、资源分配和隐私。

【关键词】联邦学习；边缘设备；边缘计算；物联网

【发表时间】2022

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】2.690

【翻译者】石东瑛