93 citations · 249 across the 31 of their papers we have counts for
11 papers · 1 filter
FEDEXCHANGE: Bridging the Domain Gap in Federated Object Detection for Free
Haolin Yuan, Jingtao Li, Weiming Zhuang +2
Federated Object Detection (FOD) enables clients to collaboratively train a global object detection model without accessing their local data from diverse domains. However, signific…
Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
Guangyu Sun, Jingtao Li, Weiming Zhuang +2
Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks, particularly in privacy-sensitive applications. Due to data privacy regulation…
Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection
Wenxiao Wang, Weiming Zhuang, Lingjuan Lyu
The advancement of deep learning technologies is bringing new models every day, motivating the study of scalable model selection. An ideal model selection scheme should minimally s…
FedMef: Towards Memory-efficient Federated Dynamic Pruning
Hong Huang, Weiming Zhuang, Chen Chen +1
Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. However, its application on resource-constrained devices is challenging due to the…
MAS: Towards Resource-Efficient Federated Multiple-Task Learning
Weiming Zhuang, Yonggang Wen, Lingjuan Lyu +1
Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous FL tas…
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Sikai Bai, Shuaicheng Li, Weiming Zhuang +7
Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fracti…
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
Weiming Zhuang, Chen Chen, Jingtao Li +3
The intersection of Foundation Model (FM) and Federated Learning (FL) presents a unique opportunity to unlock new possibilities for real-world applications. On the one hand, FL, as…
FedWon: Triumphing Multi-domain Federated Learning Without Normalization
Weiming Zhuang, Lingjuan Lyu
Federated learning (FL) enhances data privacy with collaborative in-situ training on decentralized clients. Nevertheless, FL encounters challenges due to non-independent and identi…
TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation
Jie Zhang, Chen Chen, Weiming Zhuang +1
This paper focuses on an under-explored yet important problem: Federated Class-Continual Learning (FCCL), where new classes are dynamically added in federated learning. Existing FC…
Smart Multi-tenant Federated Learning
Weiming Zhuang, Yonggang Wen, Shuai Zhang
Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous traini…
Divergence-aware Federated Self-Supervised Learning
Weiming Zhuang, Yonggang Wen, Shuai Zhang
Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn…