FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems-Reference-Cited by-同舟云学术

FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems

Published:2020-08-20 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Muhammad Khalil¹,Wang Qinqin¹,O'Reilly-Morgan Diarmuid¹,Tragos Elias¹,Smyth Barry¹,Hurley Neil¹,Geraci James²,Lawlor Aonghus¹

Affiliation:

1. University College Dublin, Dublin, Ireland

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

Funder

Science Foundation Ireland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394486.3403176

Reference29 articles.

1. Identifying representative users in matrix factorization-based recommender systems: application to solving the content-less new item cold-start problem

2. Fei Chen Mi Luo Zhenhua Dong Zhenguo Li and Xiuqiang He. 2018. Federated Meta-Learning with Fast Convergence and Efficient Communication. arxiv: cs.LG/1802.07876 Fei Chen Mi Luo Zhenhua Dong Zhenguo Li and Xiuqiang He. 2018. Federated Meta-Learning with Fast Convergence and Efficient Communication. arxiv: cs.LG/1802.07876

3. On Sampling Strategies for Neural Network-based Collaborative Filtering

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