A Survey on the use of Federated Learning in Privacy-Preserving Recommender Systems

In the age of information overload, recommender systems have emerged as essential tools, assisting users in decision-making processes by offering personalized suggestions. However, their effectiveness is contingent on the availability of large amounts of user data, raising significant privacy and se...

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Veröffentlicht in:	IEEE open journal of the Computer Society 2024, Vol.5, p.227-247
Hauptverfasser:	Chronis, Christos, Varlamis, Iraklis, Himeur, Yassine, Sayed, Aya N., AL-Hasan, Tamim M., Nhlabatsi, Armstrong, Bensaali, Faycal, Dimitrakopoulos, George
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer architecture Data privacy distributed learning Federated learning federated learning (FL) Machine learning Privacy privacy-preserving techniques Recommender systems Security Surveys
Online-Zugang:	Volltext
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