355 citations · 569 across the 25 of their papers we have counts for
6 papers · 1 filter
Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
Hanieh Hashemi, Wenjie Xiong, Liu Ke +4
Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest o…
Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information
Kiwan Maeng, Chuan Guo, Sanjay Kariyappa +1
Split learning and inference propose to run training/inference of a large model that is split across client devices and the cloud. However, such a model splitting imposes privacy c…
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis
Sanjay Kariyappa, Chuan Guo, Kiwan Maeng +4
Federated learning (FL) aims to perform privacy-preserving machine learning on distributed data held by multiple data owners. To this end, FL requires the data owners to perform tr…
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…
Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka +5
Technology companies have been leading the way to a renewable energy transformation, by investing in renewable energy sources to reduce the carbon footprint of their datacenters. I…