12 citations · 32 across the 7 of their papers we have counts for
8 papers
Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization
Guopeng Lin, Weili Han, Wenqiang Ruan +4
Multi-party training frameworks for decision trees based on secure multi-party computation enable multiple parties to train high-performance models on distributed private data with…
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
Xin-Chun Li, Shaoming Song, Yinchuan Li +4
In some real-world applications, data samples are usually distributed on local devices, where federated learning (FL) techniques are proposed to coordinate decentralized clients wi…
ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation
Yan Zhuang, Zhenzhe Zheng, Yunfeng Shao +3
Pervasive mobile AI applications primarily employ one of the two learning paradigms: cloud-based learning (with powerful large models) or on-device learning (with lightweight small…
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Xin-Chun Li, Wen-Shu Fan, Shaoming Song +4
Knowledge Distillation (KD) aims at transferring the knowledge of a well-performed neural network (the {\it teacher}) to a weaker one (the {\it student}). A peculiar phenomenon is…
Federated Learning with Position-Aware Neurons
Xin-Chun Li, Yi-Chu Xu, Shaoming Song +4
Federated Learning (FL) fuses collaborative models from local nodes without centralizing users' data. The permutation invariance property of neural networks and the non-i.i.d. data…
Domain Adaptation without Model Transferring
Kunhong Wu, Yucheng Shi, Yahong Han +3
In recent years, researchers have been paying increasing attention to the threats brought by deep learning models to data security and privacy, especially in the field of domain ad…