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.
Fedfair: Training fair models in cross-silo federated learning
2 Pith papers cite this work. Polarity classification is still indexing.
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The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.
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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A Survey on Foundation Models for Personalized Federated Intelligence
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.