Work

NodeRec+: A Lightweight Framework for Federated Recommender Systems

Year: 2025

Type: conference-paper

Abstract: Data privacy is a critical concern in today's data-driven world. To this end, Federated Learning (FL) has been researched extensively, as it allows sensitive data to be kept secure on local devices while training global Machine Learning (ML) models across multiple devices. Several FL topologies have been introduced to address different users' needs. FL fits well within the Recommender Systems (RS) domain, with decentralised large-scale datasets and user privacy issues, as it improves personalised recommendations and accuracy while keeping users' sensitive data secure.

Source: International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

DOI: 10.1145/3726302.3730138

Authors: Diarmuid O'Reilly-Morgan, Erika Duriakova, Elias Tragos, Neil Hurley, Aonghus Lawlor

Institutions: University College Dublin, Insight SFI Research Centre for Data Analytics

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze