355 citations · 569 across the 25 of their papers we have counts for
3 papers · 1 filter
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
Juntaek Lim, Youngeun Kwon, Ranggi Hwang +3
Differential privacy (DP) is widely being employed in the industry as a practical standard for privacy protection. While private training of computer vision or natural language pro…
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis +7
Federated learning (FL) has emerged as an effective approach to address consumer privacy needs. FL has been successfully applied to certain machine learning tasks, such as training…
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
Kiwan Maeng, Haiyu Lu, Luca Melis +3
Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device. While prior FL optimizations tackled…