Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

• Published in: IEEE transactions on cognitive communications and networking Vol. 8; no. 3; pp. 1566 - 1593
• Main Authors: Tu, Xuezhen, Zhu, Kun, Luong, Nguyen Cong, Niyato, Dusit, Zhang, Yang, Li, Juan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological system modeling; Completion time; Computational modeling; Data models; Data privacy; economic theories; Economics; Federated learning; game theoretic models; Game theory; Games; incentive mechanisms; Machine learning; Model accuracy; Servers; Training

Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners' raw data. In FL, the data owners can train ML models based on their local data and only send the model updates rather than raw data to the mod...