← Back to all news
arXiv Machine Learning By Sina Najafi, Mostafa Tavassolipour, Seyed Pooya Shariatpanahi

Joint Domain-Class Modeling for Federated Learning Under Feature Skew

Read the original on arXiv Machine Learning →

The paper introduces Joint Domain-Class Federated Learning (JDFL), a lightweight, optimizer‑agnostic extension designed to address feature skew in federated learning. JDFL infers pseudo‑domains from local update signals and expands the classifier head to output joint domain‑class logits, enabling the model to capture domain‑conditioned appearance while sharing a backbone. Two supervision strategies—similarity‑aware soft‑labeling and per‑sample randomized target assignment—are proposed to train the expanded head, and experiments on domain‑shifted image benchmarks show consistent improvements in global test accuracy over standard FL methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Sep 22

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

The paper introduces FedPFT, a federated learning framework that tackles the feature‑classifier mismatch problem by using personalized prompts processed through a shared self‑attention transformation module. Unlike prior methods that either degrade the feature extractor or address the mismatch only after training, FedPFT aligns local features with the global classifier during training, improving aggregation and model performance. Experiments demonstrate that FedPFT surpasses state‑of‑the‑art methods by up to 5.07%, and gains up to 7.08% when combined with collaborative contrastive learning.

By Xinghao Wu, Xuefeng Liu, Jianwei Niu, Guogang Zhu, Mingjia Shi, Shaojie Tang, Jing Yuan
fine-tuningbenchmarkssafety
More like this →
arXiv Machine Learning
Aug 18

FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting

arXiv:2608. 15310v1 Announce Type: cross Abstract: Multimodal data collected by heterogeneous devices are used for collaborative training, where federated learning (FL) serves as a key paradigm for effective distributed modeling with data privacy preservation.

By Zhenyan Liu, Hua Zhang, Haoran Gao, Qi Li, Hongliang Zhu, Huiyu Zhou, Zongliang Shen, Yanxin Xu, Jiahui Wang
multimodalsafety
More like this →