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cs.LG2026
Federated Learning at the Forefront of Fairness: A Multifaceted Perspective
Noorain Mukhtiar, Adnan Mahmood, Yipeng Zhou +3
Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients' constraints and balanced model performance across various scenarios. In this s…
cs.LG2025
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
Huitong Jin, Yipeng Zhou, Quan Z. Sheng +2
Differentially Private Federated Learning (DPFL) strengthens privacy protection by perturbing model gradients with noise, though at the cost of reduced accuracy. Although prior emp…
cs.LG2024
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
Jiating Ma, Yipeng Zhou, Qi Li +3
To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To…