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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4-bit quantization in federated learning for aerospace predictive maintenance preserves accuracy with 8x lower communication cost, while 2-bit quantization produces high instability under non-IID data distributions.
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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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Quantization Impact on the Accuracy and Communication Efficiency Trade-off in Federated Learning for Aerospace Predictive Maintenance
4-bit quantization in federated learning for aerospace predictive maintenance preserves accuracy with 8x lower communication cost, while 2-bit quantization produces high instability under non-IID data distributions.