Q-LocalAdam reduces optimizer memory by 3.37x via tailored 8-bit quantization for Adam states while maintaining or improving accuracy under data heterogeneity in edge federated learning.
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RESFL integrates adversarial feature disentanglement and uncertainty-aware client weighting in federated learning to reduce membership inference attacks and equality-of-opportunity gaps while preserving model utility for object detection.
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Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning
Q-LocalAdam reduces optimizer memory by 3.37x via tailored 8-bit quantization for Adam states while maintaining or improving accuracy under data heterogeneity in edge federated learning.
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RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility
RESFL integrates adversarial feature disentanglement and uncertainty-aware client weighting in federated learning to reduce membership inference attacks and equality-of-opportunity gaps while preserving model utility for object detection.