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Federated learning has emerged as an important paradigm for training machine learning models in different domains.
Das fehlergesetz und seine verallgemeinerungen durch fechner und pearson. a rejoinder [the error law and its generalizations by fechner and pearson. a rejoinder]
Karl Pearson · 1905
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On random graphs. 𝕀 \mathbb{I}
Paul Erdős and Alfréd Rényi · 1959
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Random graphs
Edgar Gilbert · 1959
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On lipschitz embedding of finite metric spaces in hilbert space
Jean Bourgain · 1985
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A simple min-cut algorithm
Mechthild Stoer and Frank Wagner · 1997
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Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication
Jurij Leskovec, Deepayan Chakrabarti, Jon Kleinberg, and Christos Faloutsos · 2005
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Fast subtree kernels on graphs
Nino Shervashidze and Karsten M. Borgwardt · 2009
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Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
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Deep graph kernels
Pinar Yanardag and S.V.N. Vishwanathan · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Communication-efficient learning of deep networks from decentralized data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Simultaneous inference for misaligned multivariate functional data
Niels Lundtorp Olsen, Bo Markussen, and Lars Lau Rakêt · 2017
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Federated meta-learning with fast convergence and efficient communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
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Anonymous walk embeddings
Sergey Ivanov and Evgeny Burnaev · 2018
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Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data
Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim · 2018
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Node, motif and subgraph: Leveraging network functional blocks through structural convolution
Carl Yang, Mengxiong Liu, Vincent W Zheng, and Jiawei Han · 2018
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Hierarchical graph representation learning with differentiable pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2019
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Peer-to-peer federated learning on graphs
Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi, and Farinaz Koushanfar · 2019
Tighter theory for local sgd on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
Felix Sattler, Klaus-Robert Müller, and Wojciech Samek · 2020
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Graphfl: A federated learning framework for semi-supervised node classification on graphs
Binghui Wang, Ang Li, Hai Li, and Yiran Chen · 2020
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Variance reduced local sgd with lower communication complexity
Xianfeng Liang, Shuheng Shen, Jingchang Liu, Zhen Pan, Enhong Chen, and Yifei Cheng · 2019
Cited alongside, same era.
Adaptive federated learning in resource constrained edge computing systems
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K Leung, Christian Makaya, Ting He, and Kevin Chan · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr, Christopher Fifty, Tao Yu, and Kilian Q Weinberger · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
Conditional structure generation through graph variational generative adversarial nets
Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, and Pan Li · 2019
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Position-aware graph neural networks
Jiaxuan You, Rex Ying, and Jure Leskovec · 2019
Cited alongside, same era.
Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
Cited alongside, same era.
Heterogeneous network representation learning: A unified framework with survey and benchmark
Carl Yang, Yuxin Xiao, Yu Zhang, Yizhou Sun, and Jiawei Han · 2020
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Co-embedding network nodes and hierarchical labels with taxonomy based generative adversarial nets
Carl Yang, Jieyu Zhang, and Jiawei Han · 2020
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Vertically federated graph neural network for privacy-preserving node classification
Jun Zhou, Chaochao Chen, Longfei Zheng, Huiwen Wu, Jia Wu, Xiaolin Zheng, Bingzhe Wu, Ziqi Liu, and Li Wang · 2020
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Cluster-driven graph federated learning over multiple domains
Debora Caldarola, Massimiliano Mancini, Fabio Galasso, Marco Ciccone, Emanuele Rodolà, and Barbara Caputo · 2021
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Personalized federated learning with moreau envelopes
Canh T. Dinh, Nguyen H. Tran, and Tuan Dung Nguyen · 2021
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Fedgraphnn: A federated learning system and benchmark for graph neural networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr · 2021
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Personalized cross-silo federated learning on non-iid data
Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, and Yong Zhang · 2021
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Ditto: Fair and robust federated learning through personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith · 2021
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Cross-node federated graph neural network for spatio-temporal data modeling
Chuizheng Meng, Sirisha Rambhatla, and Yan Liu · 2021
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Fl-agcns: Federated learning framework for automatic graph convolutional network search
Chunnan Wang, Bozhou Chen, Geng Li, and Hongzhi Wang · 2021
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Fedgnn: Federated graph neural network for privacy-preserving recommendation
Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, and Xing Xie · 2021
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2021
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Subgraph federated learning with missing neighbor generation
Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, and Siu Ming Yiu · 2021
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