Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Author:

Witt Leon1ORCID,Heyer Mathis2,Toyoda Kentaroh3ORCID,Samek Wojciech4ORCID,Li Dan5ORCID

Affiliation:

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

2. Department of Industrial Engineering, Tsinghua University, Beijing, China

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

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

5. Department of Computer Science, Tsinghua University, Beijing, China

Funder

Key-Area Research and Development Program of Guangdong Province

Tsinghua University-China Mobile Communications Group Company, Ltd. Joint Institute, KAKENHI from MEXT/JSPS, Japan

German Ministry for Education and Research (BMBF) through BIFOLD

European Union’s Horizon 2020 Research and Innovation Programme through COPA EUROPE

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

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1. Distributed Learning in the IoT–Edge–Cloud Continuum;Machine Learning and Knowledge Extraction;2024-02-01

2. A survey on state-of-the-art experimental simulations for privacy-preserving federated learning in intelligent networking;Electronic Research Archive;2024

3. Benchmarking Federated Learning Frameworks for Medical Imaging Tasks;Image Analysis and Processing - ICIAP 2023 Workshops;2024

4. Privacy-Preserving and Verifiable Decentralized Federated Learning;2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE);2023-06-15

5. A Systematic Literature Review on Client Selection in Federated Learning;Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering;2023-06-14

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