2 citations · 2 across the 12 of their papers we have counts for
13 papers
A Controlled Evaluation of Curvature Inspired Scalar Feature Scaling for Domain Generalization
Sunny Gupta, Amit Sethi
Domain-specific curvature is an appealing idea for domain generalization (DG), but curvature learning can denote substantially different computations. This paper evaluates one deli…
Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models
Sunny Gupta, Shambhavi Shanker, Amit Sethi
Federated fine-tuning of foundation models using Low-Rank Adaptation (LoRA) offers a communication efficient solution for distributed learning. However, existing federated LoRA met…
BiPrompt: Bilateral Prompt Optimization for Visual and Textual Debiasing in Vision-Language Models
Sunny Gupta, Shounak Das, Amit Sethi
Vision language foundation models such as CLIP exhibit impressive zero-shot generalization yet remain vulnerable to spurious correlations across visual and textual modalities. Exis…
FedHypeVAE: Federated Learning with Hypernetwork Generated Conditional VAEs for Differentially Private Embedding Sharing
Sunny Gupta, Amit Sethi
Federated data sharing promises utility without centralizing raw data, yet existing embedding-level generators struggle under non-IID client heterogeneity and provide limited forma…
Federated Cross-Modal Style-Aware Prompt Generation
Suraj Prasad, Navyansh Mahla, Sunny Gupta +1
Prompt learning has propelled vision-language models like CLIP to excel in diverse tasks, making them ideal for federated learning due to computational efficiency. However, convent…
FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching
Sunny Gupta, Nikita Jangid, Shounak Das +1
Domain Generalization (DG) seeks to train models that perform reliably on unseen target domains without access to target data during training. While recent progress in smoothing th…
UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data
Sunny Gupta, Nikita Jangid, Amit Sethi
Federated Learning (FL) often suffers from severe performance degradation when faced with non-IID data, largely due to local classifier bias. Traditional remedies such as global mo…
FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment
Sunny Gupta, Vinay Sutar, Varunav Singh +1
Federated Learning (FL) offers a decentralized paradigm for collaborative model training without direct data sharing, yet it poses unique challenges for Domain Generalization (DG),…
Sequential Compression Layers for Efficient Federated Learning in Foundational Models
Navyansh Mahla, Sunny Gupta, Amit Sethi
Federated Learning (FL) has gained popularity for fine-tuning large language models (LLMs) across multiple nodes, each with its own private data. While LoRA has been widely adopted…
Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-Tailed Class Imbalance
Pankhi Kashyap, Pavni Tandon, Sunny Gupta +3
Long-tailed problems in healthcare emerge from data imbalance due to variability in the prevalence and representation of different medical conditions, warranting the requirement of…
FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator
Sunny Gupta, Nikita Jangid, Amit Sethi
Federated Learning (FL) facilitates data privacy by enabling collaborative in-situ training across decentralized clients. Despite its inherent advantages, FL faces significant chal…
FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch
Sunny Gupta, Mohit Jindal, Pankhi Kashyap +2
Federated learning faces a critical challenge in balancing communication efficiency with rapid convergence, especially for second-order methods. While Newton-type algorithms achiev…
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging
Sunny Gupta, Amit Sethi
Federated Learning (FL) offers a privacy-preserving approach to train models on decentralized data. Its potential in healthcare is significant, but challenges arise due to cross-cl…