On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems. (arXiv:2201.10382v1 [cs.LG])

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 on-device learning personalization recommender systems systems