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Srinivasa Pranav

3 accepted papers

2025

FedBaF: Federated Learning Aggregation Biased by a Foundation Model

AISTATS 2025poster

Foundation models are now a major focus of leading technology organizations due to their ability to generalize across diverse tasks. Existing approaches for adapting foundation models to new applications often rely on Federated Learning (FL) and disclose the foundation model weights to clients when…

Cited by 0SourceScholar
2025

Peer-to-Peer Learning Dynamics of Wide Neural Networks

ICASSP 2025accepted

Peer-to-peer learning is an increasingly popular framework that enables beyond-5G distributed edge devices to collaboratively train deep neural networks in a privacy-preserving manner without the aid of a central server. Neural network training algorithms for emerging environments, e.g., smart citie…

Cited by 0SourceScholar
2023

Learning Gradients of Convex Functions with Monotone Gradient Networks

ICASSP 2023accepted

While much effort has been devoted to deriving and analyzing effective convex formulations of signal processing problems, the gradients of convex functions also have critical applications ranging from gradient-based optimization to optimal transport. Recent works have explored data-driven methods fo…

Cited by 0SourceScholar