Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning-Reference-Cited by-同舟云学术

Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning

Published:2020 Issue: Volume: Page:317-332
ISSN:1865-0929
Container-title:Machine Learning and Knowledge Discovery in Databases
language:
Short-container-title:

Author:

Hegedűs István^ORCID,Danner Gábor^ORCID,Jelasity Márk^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-43823-4_27

Reference17 articles.

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2. Berta, Á., Bilicki, V., Jelasity, M.: Defining and understanding smartphone churn over the internet: a measurement study. In: Proceedings of the 14th IEEE International Conference on Peer-to-Peer Computing (P2P 2014). IEEE (2014). https://doi.org/10.1109/P2P.2014.6934317

3. Bonawitz, K., et al.: Practical secure aggregation for federated learning on user-held data. In: NIPS Workshop on Private Multi-Party Machine Learning (2016)

4. Chen, F., Dong, Z., Li, Z., He, X.: Federated meta-learning for recommendation. CoRR abs/1802.07876 (2018). http://arxiv.org/abs/1802.07876

5. Danner, G., Berta, Á., Hegedűs, I., Jelasity, M.: Robust fully distributed mini-batch gradient descent with privacy preservation. Secur. Commun. Netw. 2018, 6728020 (2018). https://doi.org/10.1155/2018/6728020

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