SCC-VFL reduces individual decision flip rates by up to 98% in vertical federated learning while preserving accuracy through differentially private feature role discovery and selective counterfactual consistency enforcement.
A survey on vertical federated learning: From a layered perspective
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FedHF-Impute enables federated imputation across heterogeneous feature spaces by using a shared global feature graph and message passing for indirect cross-client knowledge transfer, reporting RMSE gains on SECOM and AirQuality datasets.
citing papers explorer
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Toward Individual Fairness Without Centralized Data: Selective Counterfactual Consistency for Vertical Federated Learning
SCC-VFL reduces individual decision flip rates by up to 98% in vertical federated learning while preserving accuracy through differentially private feature role discovery and selective counterfactual consistency enforcement.
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Federated Imputation under Heterogeneous Feature Spaces
FedHF-Impute enables federated imputation across heterogeneous feature spaces by using a shared global feature graph and message passing for indirect cross-client knowledge transfer, reporting RMSE gains on SECOM and AirQuality datasets.