FedQuad uses quadruplet constraints and stochastic client selection in federated learning to reduce representation misalignment and improve generalization on heterogeneous data.
Orchestra: Unsupervised federated learning via globally consistent clustering
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A new distributed optimization method enforces diverse and discriminative representations via variance constraints for i.i.d. data and node clustering for non-i.i.d. data, with theoretical guarantees and semantic sharing.
Survey categorizing DL methods for distribution shifts in MedIA by clinical scenarios, with analysis indicating constrained gains as domain information decreases and a shift toward uncertainty-aware modeling.
citing papers explorer
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Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis
FedQuad uses quadruplet constraints and stochastic client selection in federated learning to reduce representation misalignment and improve generalization on heterogeneous data.
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Semantic-based Distributed Learning for Diverse and Discriminative Representations
A new distributed optimization method enforces diverse and discriminative representations via variance constraints for i.i.d. data and node clustering for non-i.i.d. data, with theoretical guarantees and semantic sharing.
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Navigating Distribution Shifts in Medical Image Analysis: A Survey
Survey categorizing DL methods for distribution shifts in MedIA by clinical scenarios, with analysis indicating constrained gains as domain information decreases and a shift toward uncertainty-aware modeling.