5 papers · 1 filter
What's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity
Kumar Kshitij Patel, Rustem Islamov, Sebastian U Stich +3
Local SGD, also known as Federated Averaging, is a widely used distributed optimization algorithm. Although Local SGD often outperforms alternatives such as Mini-batch SGD in pract…
Distributionally Robust Linear Regression With Block Lewis Weights
Naren Sarayu Manoj, Kumar Kshitij Patel
We present an algorithm for the group distributionally robust (GDR) least squares problem. Given groups, a parameter vector in , and stacked design matrices and r…
What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness
Kumar Kshitij Patel
This thesis contributes to the theoretical understanding of local update algorithms, especially Local SGD, in distributed and federated optimization under realistic models of data…
Personalized Federated Training of Diffusion Models with Privacy Guarantees
Kumar Kshitij Patel, Weitong Zhang, Lingxiao Wang
The scarcity of accessible, compliant, and ethically sourced data presents a considerable challenge to the adoption of artificial intelligence (AI) in sensitive fields like healthc…
The Limitations of Model Retraining in the Face of Performativity
Anmol Kabra, Kumar Kshitij Patel
We study stochastic optimization in the context of performative shifts, where the data distribution changes in response to the deployed model. We demonstrate that naive retraining…