12 papers
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…
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…
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…