Fetching the paper…
Reading the bibliography…
We consider a foundational unsupervised learning task of $k$-means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients.
The Hungarian method for the assignment problem
Harold W Kuhn · 1955
Earlier work this paper cites.
Least squares quantization in PCM
Stuart Lloyd · 1982
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
A data-clustering algorithm on distributed memory multiprocessors
Inderjit S. Dhillon and Dharmendra S. Modha · 1999
Earlier work this paper cites.
A fast parallel clustering algorithm for large spatial databases
Xiaowei Xu, Jochen Jäger, and Hans-Peter Kriegel · 1999
Earlier work this paper cites.
Distributed clustering using collective principal component analysis
Hillol Kargupta, Weiyun Huang, Krishnamoorthy Sivakumar, and Erik Johnson · 2001
Earlier work this paper cites.
A local search approximation algorithm for k k -means clustering
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu · 2002
Earlier work this paper cites.
Parallel K K -Means algorithm on distributed memory multiprocessors
Manasi N. Joshi · 2003
Earlier work this paper cites.
Towards effective and efficient distributed clustering
Eshref Januzaj, Hans-Peter Kriegel, and Martin Pfeifle · 2003
Earlier work this paper cites.
Privacy-preserving k k -means clustering over vertically partitioned data
Jaideep Vaidya and Chris Clifton · 2003
Earlier work this paper cites.
On coresets for k k -means and k k -median clustering
Sariel Har-Peled and Soham Mazumdar · 2004
Earlier work this paper cites.
Outlier detection using clustering methods: a data cleaning application
Antonio Loureiro, Luis Torgo, and Carlos Soares · 2004
Earlier work this paper cites.
Privacy-preserving distributed k k -means clustering over arbitrarily partitioned data
Geetha Jagannathan and Rebecca N Wright · 2005
Earlier work this paper cites.
How slow is the k k -means method?
David Arthur and Sergei Vassilvitskii · 2006
Earlier work this paper cites.
Privacy-preserving set union
Keith Frikken · 2007
Earlier work this paper cites.
Secure two-party k k -means clustering
Paul Bunn and Rafail Ostrovsky · 2007
Earlier work this paper cites.
Robust estimation in the normal mixture model based on robust clustering
JA Cuesta-Albertos, C Matrán, and A Mayo-Iscar · 2008
Earlier work this paper cites.
Clustering-based outlier detection method
Sheng-yi Jiang and Qing-bo An · 2008
Earlier work this paper cites.
Fast polynomial factorization and modular composition
Kiran S. Kedlaya and Christopher Umans · 2011
Cited alongside, same era.
Constant-round multi-party private set union using reversed laurent series
Jae Hong Seo, Jung Hee Cheon, and Jonathan Katz · 2012
Cited alongside, same era.
Improved spectral-norm bounds for clustering
Pranjal Awasthi and Or Sheffet · 2012
Cited alongside, same era.
An efficient approach for privacy preserving distributed k k -means clustering based on shamir’s secret sharing scheme
Sankita Patel, Sweta Garasia, and Devesh Jinwala · 2012
Cited alongside, same era.
Distributed k k -means and k k -median clustering on general topologies
Maria-Florina F. Balcan, Steven Ehrlich, and Yingyu Liang · 2013
Cited alongside, same era.
The effectiveness of lloyd-type methods for the k k -means problem
Rafail Ostrovsky, Yuval Rabani, Leonard J. Schulman, and Chaitanya Swamy · 2013
Practical privacy-preserving k k -means clustering
Payman Mohassel, Mike Rosulek, and Ni Trieu · 2019
Later among the works it cites.
The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R. Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus Maier-Hein, et al · 2020
Later among the works it cites.
Personalized federated learning with Moreau envelopes
Canh T. Dinh, Nguyen Tran, and Josh Nguyen · 2020
Later among the works it cites.
Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
Later among the works it cites.
Three approaches for personalization with applications to federated learning
Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A comparative study of efficient initialization methods for the k k -means clustering algorithm
M. Emre Celebi, Hassan A. Kingravi, and Patricio A. Vela · 2013
Cited alongside, same era.
Agnostic estimation of mean and covariance
Kevin A Lai, Anup B Rao, and Santosh Vempala · 2016
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
Cited alongside, same era.
Practical privacy-preserving mapreduce based k k -means clustering over large-scale dataset
Jiawei Yuan and Yifan Tian · 2017
Cited alongside, same era.
Applied federated learning: Improving Google keyboard query suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
Cited alongside, same era.
An efficient framework for clustered federated learning
Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran · 2020
Later among the works it cites.
Inverting gradients-how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 2020
Later among the works it cites.
Differentially private set union
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, and Sergey Yekhanin · 2020
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 · 2020
Later among the works it cites.
Diverse image generation via self-conditioned gans
Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, and Antonio Torralba · 2020
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.
ClusterFL: a similarity-aware federated learning system for human activity recognition
Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Jianwei Huang, and Guoliang Xing · 2021
Later among the works it cites.
Dynamic clustering in federated learning
Yeongwoo Kim, Ezeddin Al Hakim, Johan Haraldson, Henrik Eriksson, José Mairton B. da Silva, and Carlo Fischione · 2021
Later among the works it cites.
Heterogeneity for the win: One-shot federated clustering
Don Kurian Dennis, Tian Li, and Virginia Smith · 2021
Later among the works it cites.
Robbing the Fed: Directly obtaining private data in federated learning with modified models
Liam Fowl, Jonas Geiping, Wojtek Czaja, Micah Goldblum, and Tom Goldstein · 2021
Later among the works it cites.
Federated unsupervised clustering with generative models
Jichan Chung, Kangwook Lee, and Kannan Ramchandran · 2022
Closest in time.
Diverse client selection for federated learning via submodular maximization
Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, and Jeff Bilmes · 2022
Closest in time.