Partial model sharing in federated conformal prediction protects against Byzantine clients in both training and calibration, yielding closer-to-nominal coverage and tighter intervals than standard approaches.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.LG 2verdicts
UNVERDICTED 2roles
background 2polarities
background 2representative citing papers
A survey that categorizes uncertainty quantification approaches for graphical models into representation and handling dimensions to identify challenges and opportunities.
citing papers explorer
-
Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Prediction
Partial model sharing in federated conformal prediction protects against Byzantine clients in both training and calibration, yielding closer-to-nominal coverage and tighter intervals than standard approaches.
-
Uncertainty Quantification on Graph Learning: A Survey
A survey that categorizes uncertainty quantification approaches for graphical models into representation and handling dimensions to identify challenges and opportunities.