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stat.ML2026
Personalized Federated Learning via Variance-Aware Nonparametric Empirical Bayes
Jae Ho Chang, Arnab Auddy, Subhadeep Paul
We develop a new approach to Personalized Federated Learning across heterogeneous clients using Nonparametric Empirical Bayes (NPEB). Leveraging the asymptotic normality of local p…
math.ST2026
Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering
Arkajyoti Bhattacharjee, Arnab Auddy
Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexpl…
stat.ML2026
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
Arnab Auddy, Xiangni Peng, Subhadeep Paul
Federated Learning is a leading framework for training ML and AI models collaboratively across numerous user devices or databases. We study the trade-offs among estimation accuracy…