A quantization-assisted Gaussian differential privacy mechanism and a min-max fair scheduling algorithm for wireless personalized federated learning, backed by convergence bounds and simulations showing up to 87% accuracy improvement.
P2cefl: Privacy-preserving and commu- nication efficient federated learning with sparse gradient and dithering quantization,
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Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
A quantization-assisted Gaussian differential privacy mechanism and a min-max fair scheduling algorithm for wireless personalized federated learning, backed by convergence bounds and simulations showing up to 87% accuracy improvement.