论文标题
分散和激励的联合学习框架：系统文献综述
Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review
论文作者
论文摘要
联邦学习（FL）的出现点燃了一个新的范式，用于平行和机密的分散机器学习（ML），具有利用大量物联网，移动设备，移动和边缘设备的计算能力，而无需数据离开各自的设备，从而确保按设计进行隐私。然而，为了将这种新范式扩展到已经委托大众采用的一小部分实体之外，联合学习框架（FLF）必须成为（i）真正分散的，并且（ii）必须激励参与者。这是第一个系统的文献综述，分析了这两个领域，分散和激励的联邦学习的整体FLF。通过查询12个主要的科学数据库，检索了422个出版物。最后，在进行系统的审查和过滤过程后，仍保留40篇文章，以进行深入检查。尽管有巨大的潜力来指导更分布和确保AI的未来，但没有分析的FLF尚未准备就绪。这些方法在用例，系统设计，解决问题和彻底性方面有很大的变化。我们是第一个提供系统的方法来分类和量化FLF之间的差异，揭示当前工作的局限性并在这个新领域中的研究方向进行分类和量化。
The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.
