A Hybrid Decentralised Learning Topology for Recommendations with Improved Privacy

Author:

Morgan Diarmuid O'Reilly1ORCID,Tragos Elias1ORCID,Geraci James2ORCID,Wang Qinqin1ORCID,Hurley Neil1ORCID,Smyth Barry1ORCID,Lawlor Aonghus1ORCID

Affiliation:

1. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

2. Samsung Electronics Co. Ltd, Seoul, Republic of Korea

Funder

Science Foundation Ireland

Publisher

ACM

Reference31 articles.

1. Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020. How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics. 2938--2948.

2. Secure Federated Matrix Factorization

3. Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, and Xiaolong Li. 2018. Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization. In AAAI Conference on Artificial Intelligence.

4. Chen Chen, Jingfeng Zhang, Anthony KH Tung, Mohan Kankanhalli, and Gang Chen. 2020. Robust federated recommendation system. preprint arXiv:2006.08259 (2020).

5. Erika Duriakova, Weipeng Huang, Elias Tragos, Aonghus Lawlor, Barry Smyth, James Geraci, and Neil Hurley. 2020. An Algorithmic Framework for Decentralised Matrix Factorisation. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Springer.

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