Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications.
Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications.
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DOI：
10.1109/access.2020.3013541
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Saeed F
中科院分区：
文献类型：
--
作者：
Aledhari M;Razzak R;Parizi RM;Saeed F
本文对联合学习(FL)进行了全面的研究，重点介绍了使能软硬件平台、协议、实际应用程序和用例。FL可以适用于多个领域，但将其应用于不同行业有其自身的一系列障碍。FL被称为协作学习，算法(S)通过分散的数据样本在多个设备或服务器上进行训练，而不必交换实际数据。这种方法从根本上不同于其他更成熟的技术，例如将数据样本上传到服务器或以某种形式的分布式基础设施存储数据。另一方面，FL在不共享数据的情况下生成更强大的模型，从而产生具有更高安全性和数据访问权限的隐私保护解决方案。本文首先对外语教学进行了概述。然后，概述了与FL使能技术、协议和应用相关的技术细节。与该领域的其他调查论文相比，我们的目标是提供对FL最相关的协议、平台和实际使用案例的更全面的总结，以使数据科学家能够为迫切需要FL的行业构建更好的隐私保护解决方案。我们还提供了最近文献中提出的关键挑战的概述，并提供了相关研究工作的摘要。此外，我们还探讨了FL的挑战和优势，并提供了详细的服务用例，以说明使用FL的不同架构和协议如何结合在一起以交付所需的结果。
This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.
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