Web: http://arxiv.org/abs/2201.10382

Jan. 26, 2022, 2:11 a.m. | Renjie Gu, Chaoyue Niu, Yikai Yan, Fan Wu, Shaojie Tang, Rongfeng Jia, Chengfei Lyu, Guihai Chen

cs.LG updates on arXiv.org arxiv.org

Data heterogeneity is an intrinsic property of recommender systems, making
models trained over the global data on the cloud, which is the mainstream in
industry, non-optimal to each individual user's local data distribution. To
deal with data heterogeneity, model personalization with on-device learning is
a potential solution. However, on-device training using a user's small size of
local samples will incur severe overfitting and undermine the model's
generalization ability. In this work, we propose a new device-cloud
collaborative learning framework, called …

arxiv augmentation cloud data learning model on-device learning personalization recommender systems systems

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