Cited By
View all- Zhang XFan LWang SLi WChen KYang Q(2024)A Game-theoretic Framework for Privacy-preserving Federated LearningACM Transactions on Intelligent Systems and Technology10.1145/365604915:3(1-35)Online publication date: 10-Apr-2024
In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of privacy leakage cannot be ignored in the presence of semi-honest adversaries. Existing research has focused either on designing protection ...
Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the opposing requirements in ...
In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data must be kept secure as ...
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