Application · Distribution and feature matching
GDD-FL
Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
Yuchen Tian, Jiacheng Wang, Yueming Jin, Liansheng Wang
MICCAI 2023 Workshop · first public 2023-01-01
In one paragraph
GDD-FL condenses thousands of skin-lesion images per client into one synthetic image per class, modeling dataset features as an uncertain Gaussian distribution so the synthetic images capture diverse semantics and resist distribution drift across clients; only these few synthesized images are transmitted once to train a global model. Reports reduced communication cost with performance superior to classical federated learning and existing dataset-distillation baselines.
Where it sits
- Distribution and feature matching (Surrogate matching)
- Setting: Image classification
BibTeX (generated; prefer the venue's official entry)
@article{tian2023communication,
title = {Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation},
author = {Yuchen Tian and Jiacheng Wang and Yueming Jin and Liansheng Wang},
journal = {MICCAI 2023 Workshop},
year = {2023}
}Nearby in Distribution and feature matching
RAHA — Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation
Jongoh Jeong, Sun-Kyung Lee, Kuk-Jin Yoon · ECCV 2026notableVision–languagepaper ↗code ↗
MDM — Multimodal Distribution Matching for Vision-Language Dataset Distillation
Jongoh Jeong, Hoyong Kwon, Minseok Kim et al. · CVPR 2026notableVision–languagepaper ↗code ↗
Harmonic Dataset Distillation for Time Series Forecasting
Seungha Hong, Sanghwan Jang, Wonbin Kweon et al. · AAAI 2026notableTime seriespaper ↗
Algorithmic Guarantees for Distilling Supervised and Offline RL Datasets
Aaryan Gupta, Rishi Saket, Aravindan Raghuveer · ICLR 2026notableOther datapaper ↗