Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

IEEE Internet of Things Journal, vol. 10, pp. 3642–3663

Abstract

The advent of federated learning (FL) has sparked a new paradigm of parallel and confidential decentralized machine learning (ML) with the potential of utilizing the computational power of a vast number of Internet of Things (IoT), mobile, and edge devices without data leaving the respective device, thus ensuring privacy by design. Yet, simple FL frameworks (FLFs) naively assume an honest central server and altruistic client participation. In order to scale this new paradigm beyond small groups of already entrusted entities toward mass adoption, FLFs must be: 1) truly decentralized and 2) incentivized to participants. This systematic literature review is the first to analyze FLFs that holistically apply both, the blockchain technology to decentralize the process and reward mechanisms to incentivize participation. 422 publications were retrieved by querying 12 major scientific databases. After a systematic filtering process, 40 articles remained for an in-depth examination following our five research questions. To ensure the correctness of our findings, we verified the examination results with the respective authors. Although having the potential to direct the future of distributed and secure artificial intelligence, none of the analyzed FLFs is production ready. The approaches vary heavily in terms of use cases, system design, solved issues, and thoroughness. We provide a systematic approach to classify and quantify differences between FLFs, expose limitations of current works and derive future directions for research in this novel domain.

Authors 5

1. Leon Witt

   Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute · Tsinghua University

   Affiliation as printed

   Department of Computer Science and Technology, Tsinghua University, Beijing, China

   Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany

2. Mathis Heyer Aachen Aachener Verfahrenstechnik (AVT)

   RWTH Aachen University · Tsinghua University

   Affiliation as printed

   Department of Industrial Engineering, Tsinghua University, Beijing, China

   Aachener Verfahrenstechnik (AVT), RWTH Aachen University, Aachen, Germany

3. Kentaroh Toyoda

   Agency for Science, Technology and Research · Keio University · Institute of High Performance Computing

   Affiliation as printed

   Institute of High Performance Computing, Agency for Science, Technology and Research, Connexis, Singapore

   Faculty of Science and Technology, Keio University, Yokohama, Kanagawa, Japan

4. Wojciech Samek

   Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute · Berlin Institute for the Foundations of Learning and Data · Technische Universität Berlin

   Affiliation as printed

   Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany

   Berlin Institute for the Foundations of Learning and Data, Berlin, Germany

   Department of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany

5. Dan Li

   Tsinghua University

   Affiliation as printed

   Department of Computer Science, Tsinghua University, Beijing, China