Introduces a multitask adversarial model that learns representations hiding sensitive attributes while preserving task information to balance fairness, privacy, and accuracy with minimal performance loss.
& Spigler, G
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FL with homomorphic encryption matches centralized ML performance for CVD risk prediction but adds cryptographic overhead, while DP-FL has lower cost yet greater accuracy loss especially for logistic regression.
citing papers explorer
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Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems
Introduces a multitask adversarial model that learns representations hiding sensitive attributes while preserving task information to balance fairness, privacy, and accuracy with minimal performance loss.
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Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling
FL with homomorphic encryption matches centralized ML performance for CVD risk prediction but adds cryptographic overhead, while DP-FL has lower cost yet greater accuracy loss especially for logistic regression.