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What is Incentive Mechanism for Reliable Federated Learning about?

The paper presents a reputation- and contract-based incentive mechanism aimed at enhancing reliability in Federated Learning (FL) by addressing issues of low-quality or malicious updates from data owners. It combines reputation systems and contract theory to incentivize honest contributions, demonstrating improved model accuracy and convergence through simulations. However, it faces limitations such as simplified assumptions, scalability challenges, and a lack of real-world deployment validation.

Author

NAFI ALIF

Language

EN