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The increasingly stringent regulations on privacy protection have sparked interest in federated learning.
“Recommender systems survey,”
Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez, · 2013
Earlier work this paper cites.
“Federated learning: Strategies for improving communication efficiency,”
Jakub Konecný, H. B. McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon, · 2016
Earlier work this paper cites.
“Communication-efficient learning of deep networks from decentralized data,”
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas, · 2017
Earlier work this paper cites.
“Neural collaborative filtering,”
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua, · 2017
Earlier work this paper cites.
“Federated multi-task learning,”
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar, · 2017
Earlier work this paper cites.
“Discrete deep learning for fast content-aware recommendation,”
Yan Zhang, Hongzhi Yin, Zi Huang, Xingzhong Du, Guowu Yang, and Defu Lian, · 2018
Earlier work this paper cites.
“Federated collaborative filtering for privacy-preserving personalized recommendation system,”
Muhammad Ammad-ud-din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan, · 2019
Earlier work this paper cites.
“Federated machine learning: Concept and applications,”
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong, · 2019
Earlier work this paper cites.
“Agnostic federated learning,”
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh, · 2019
Earlier work this paper cites.
“Federated learning: Challenges, methods, and future directions,”
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith, · 2020
Earlier work this paper cites.
“Fedfast: Going beyond average for faster training of federated recommender systems,”
Khalil Muhammad, Qinqin Wang, Diarmuid O’Reilly-Morgan, Elias Z. Tragos, Barry Smyth, Neil Hurley, James Geraci, and Aonghus Lawlor, · 2020
Earlier work this paper cites.
“On the convergence of fedavg on non-iid data,”
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang, · 2020
Cited alongside, same era.
“Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach,”
Alireza Fallah, Aryan Mokhtari, and Asuman E. Ozdaglar, · 2020
Cited alongside, same era.
“Fedhealth: A federated transfer learning framework for wearable healthcare,”
Yiqiang Chen, Xin Qin, Jindong Wang, Chaohui Yu, and Wen Gao, · 2020
Cited alongside, same era.
“Fair resource allocation in federated learning,”
Tian Li, Maziar Sanjabi, and Virginia Smith, · 2020
Cited alongside, same era.
“Personalized federated learning with moreau envelopes,”
Canh T. Dinh, Nguyen H. Tran, and Tuan Dung Nguyen, · 2020
Cited alongside, same era.
“Lower bounds and optimal algorithms for personalized federated learning,”
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, and Peter Richtárik, · 2020
“A quantitative metric for privacy leakage in federated learning,”
Yong Liu, Xinghua Zhu, Jianzong Wang, and Jing Xiao, · 2021
Later among the works it cites.
“Deep learning for recommender systems: A netflix case study,”
Harald Steck, Linas Baltrunas, Ehtsham Elahi, Dawen Liang, Yves Raimond, and Justin D. Basilico, · 2021
Later among the works it cites.
“Diversified point cloud classification using personalized federated learning,”
Anshun Xue, Xinghua Zhu, Jianzong Wang, and Jing Xiao, · 2021
Later among the works it cites.
“Fedrec: Federated recommendation with explicit feedback,”
Guanyu Lin, Feng Liang, Weike Pan, and Zhong Ming, · 2021
Later among the works it cites.
“Survey on federated recommendation systems,”
Zhitao Zhu, Shijing Si, Jianzong Wang, and Jing Xiao, · 2021
Later among the works it cites.
“Personalized federated learning with first order model optimization,”
Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, and José Manuel Álvarez, · 2021
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Cited alongside, same era.
“Federated learning with hierarchical clustering of local updates to improve training on non-iid data,”
Christopher Briggs, Zhong Fan, and Peter Andras, · 2020
Cited alongside, same era.
“Advances and open problems in federated learning,”
Peter Kairouz, H. B. McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, and Arjun Nitin Bhagoji et al., · 2021
Cited alongside, same era.
“Secure federated matrix factorization,”
Di Chai, Leye Wang, Kai Chen, and Qiang Yang, · 2021
Cited alongside, same era.
“Stronger privacy for federated collaborative filtering with implicit feedback,”
Lorenzo Minto, Moritz Haller, Benjamin Livshits, and Hamed Haddadi, · 2021
Cited alongside, same era.
“Fedrec++: Lossless federated recommendation with explicit feedback,”
Feng Liang, Weike Pan, and Zhong Ming, · 2021
Cited alongside, same era.
Later among the works it cites.
“Ditto: Fair and robust federated learning through personalization,”
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith, · 2021
Later among the works it cites.
“Personalized federated learning with clustering: Non-iid heart rate variability data application,”
Joo Hun Yoo, Ha Min Son, Hyejun Jeong, Eun-Hye Jang, Ah-Young Kim, Han-Young Yu, Hong Jin Jeon, and Tai-Myoung Chung, · 2021
Later among the works it cites.
“Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints,”
Felix Sattler, Klaus-Robert Müller, and Wojciech Samek, · 2021
Later among the works it cites.
“Federated neural collaborative filtering,”
Vasileios Perifanis and Pavlos S. Efraimidis, · 2022
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