1 citations · 2 across the 15 of their papers we have counts for
22 papers
SMA-DP: Spectral Memory-Aware Differential Privacy for Deep Learning
Mohammad Partohaghighi, Roummel Marcia
Differentially private stochastic gradient descent (DP-SGD) enables private deep learning through per-example clipping and calibrated Gaussian noise, but its high-variance updates…
Deep Learning under Fractional-Order Differential Privacy
Mohammad Partohaghighi, Roummel Marcia
Differentially private stochastic gradient descent (DP-SGD) is a standard approach to privacy-preserving learning based on per-example clipping, subsampling, Gaussian perturbation,…
Fractional-Order Federated Learning
Mohammad Partohaghighi, Roummel Marcia, YangQuan Chen
Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy. Despite its privacy-preserving benefits, FL has significant d…
Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling
Mohammad Partohaghighi, Roummel Marcia, Bruce J. West +1
Federated learning on connected electric vehicles (BEVs) faces severe instability due to intermittent connectivity, time-varying client participation, and pronounced client-to-clie…
Roughness-Informed Federated Learning
Mohammad Partohaghighi, Roummel Marcia, Bruce J. West +1
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, yet faces challenges in non-independent and identically distr…
When Gradient Clipping Becomes a Control Mechanism for Differential Privacy in Deep Learning
Mohammad Partohaghighi, Roummel Marcia, Bruce J. West +1
Privacy-preserving training on sensitive data commonly relies on differentially private stochastic optimization with gradient clipping and Gaussian noise. The clipping threshold is…