Feb. 17, 2022, 8:11 a.m. | Vasileios Perifanis, Pavlos S. Efraimidis

cs.LG updates on arXiv.org arxiv.org

In this work, we present a federated version of the state-of-the-art Neural
Collaborative Filtering (NCF) approach for item recommendations. The system,
named FedNCF, enables learning without requiring users to disclose or transmit
their raw data. Data localization preserves data privacy and complies with
regulations such as the GDPR. Although federated learning enables model
training without local data dissemination, the transmission of raw clients'
updates raises additional privacy issues. To address this challenge, we
incorporate a privacy-preserving aggregation method that satisfies …

arxiv collaborative collaborative filtering

Lead Developer (AI)

@ Cere Network | San Francisco, US

Research Engineer

@ Allora Labs | Remote

Ecosystem Manager

@ Allora Labs | Remote

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote