Papers Collaborative Fairness
“Collaborative Fairness” 태그가 달린 논문 7편 · 필터 해제
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
Collaborative fairness is a crucial challenge in federated learning. However, existing approaches often overlook a practical yet complex form of heterogeneity: imbalanced covariate shift. We provide a theoretical analysi…
Collaborative FairnessFairnessFederated LearningKnowledge DistillationCYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning
Collaborative learning (CL) enables multiple participants to jointly train machine learning (ML) models on decentralized data sources without raw data sharing. While the primary goal of CL is to maximize the expected acc…
Collaborative FairnessFairnessFederated LearningPrivacy-preserving gradient-based fair federated learning
Federated learning (FL) schemes allow multiple participants to collaboratively train neural networks without the need to directly share the underlying data.However, in early schemes, all participants eventually obtain th…
Collaborative FairnessFairnessFederated LearningPrivacy PreservingRedefining Contributions: Shapley-Driven Federated Learning
Federated learning (FL) has emerged as a pivotal approach in machine learning, enabling multiple participants to collaboratively train a global model without sharing raw data. While FL finds applications in various domai…
Collaborative FairnessContribution AssessmentData ValuationFairness+1FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning
Collaborative fairness stands as an essential element in federated learning to encourage client participation by equitably distributing rewards based on individual contributions. Existing methods primarily focus on adjus…
Collaborative FairnessFairnessFederated LearningA Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning
Federated learning (FL) is an emerging practical framework for effective and scalable machine learning among multiple participants, such as end users, organizations and companies. However, most existing FL or distributed…
Adversarial DefenseAdversarial RobustnessAllCollaborative Fairness+2Collaborative Fairness in Federated Learning
In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability. This problem can be addressed by Distributed or Federated Learning (FL) that l…
Collaborative FairnessFairnessFederated Learning