6 papers · 1 filter
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…
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…
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…
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),…