12 citations · 41 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
To Store or Not? Online Data Selection for Federated Learning with Limited Storage
Chen Gong, Zhenzhe Zheng, Yunfeng Shao +3
Machine learning models have been deployed in mobile networks to deal with massive data from different layers to enable automated network management and intelligence on devices. To…
Avoid Overfitting User Specific Information in Federated Keyword Spotting
Xin-Chun Li, Jin-Lin Tang, Shaoming Song +5
Keyword spotting (KWS) aims to discriminate a specific wake-up word from other signals precisely and efficiently for different users. Recent works utilize various deep networks to…
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…