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Found 4 papers, 1 papers with code
Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint
To obtain a high-quality model, an incentive mechanism is necessary to motivate more high-quality workers with data and computing power.
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement
Federated learning trains models across devices with distributed data, while protecting the privacy and obtaining a model similar to that of centralized ML.
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
We present a novel privacy-preserving federated adversarial domain adaptation approach ($\textbf{PrADA}$) to address an under-studied but practical cross-silo federated domain adaptation problem, in which the party of the target domain is insufficient in both samples and features.
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning
Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data.
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