Timezone: »
Poster
Collaborative Learning by Detecting Collaboration Partners
Shu Ding · Wei Wang
@
Massive amounts of data are naturally dispersed over different clients in many real-world applications, collaborative learning has been a promising paradigm that allows to learn models through collaboration among the clients. However, leveraging these dispersed data to learn good models is still challenging since data over different clients are heterogeneous. Previous works mainly focus on learning the centralized model for all clients or learning a personalized model for each client. When there are numerous clients, the centralized model performs badly on some clients, while learning a personalized model for each client costs unaffordable computational resources. In this paper, we propose the collaborative learning method to detect collaboration partners and adaptively learn $K$ models for numerous heterogeneous clients. We theoretically prove that the model learned for each client is a good approximation of its personalized model. Experimental results on real-world datasets verify the effectiveness of our method.
Author Information
Shu Ding (Nanjing University)
Wei Wang (Nanjing University)
More from the Same Authors
-
2023 Poster: Semi-Supervised Domain Generalization with Known and Unknown Classes »
Lei Zhang · Ji-Fu Li · Wei Wang -
2022 Spotlight: Collaborative Learning by Detecting Collaboration Partners »
Shu Ding · Wei Wang -
2022 Spotlight: Lightning Talks 3A-1 »
Shu Ding · Wanxing Chang · Jiyang Guan · Mouxiang Chen · Guan Gui · Yue Tan · Shiyun Lin · Guodong Long · Yuze Han · Wei Wang · Zhen Zhao · Ye Shi · Jian Liang · Chenghao Liu · Lei Qi · Ran He · Jie Ma · Zemin Liu · Xiang Li · Hoang Tuan · Luping Zhou · Zhihua Zhang · Jianling Sun · Jingya Wang · LU LIU · Tianyi Zhou · Lei Wang · Jing Jiang · Yinghuan Shi -
2010 Poster: Multi-View Active Learning in the Non-Realizable Case »
Wei Wang · Zhi-Hua Zhou