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When each edge device of a network only perceives a local part of the environment, collaborative inference across multiple devices is often needed to predict global properties of the environment.
Paxos made simple
Leslie Lamport · 2001
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Understanding dropout
Pierre Baldi and Peter J Sadowski · 2013
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In search of an understandable consensus algorithm
Diego Ongaro and John Ousterhout · 2014
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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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Cola: Decentralized linear learning
Lie He, An Bian, and Martin Jaggi · 2018
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Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
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Peer-to-peer federated learning on graphs
Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi, and Farinaz Koushanfar · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Splitnn-driven vertical partitioning
Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, and Ramesh Raskar · 2020
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Vafl: a method of vertical asynchronous federated learning
Tianyi Chen, Xiao Jin, Yuejiao Sun, and Wotao Yin · 2020
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Efficient asynchronous vertical federated learning via gradient prediction and double-end sparse compression
Ming Li, Yiwei Chen, Yiqin Wang, and Yu Pan · 2020
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On convergence and generalization of dropout training
Poorya Mianjy and Raman Arora · 2020
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Marten van Dijk, Nhuong V Nguyen, Toan N Nguyen, Lam M Nguyen, Quoc Tran-Dinh, and Phuong Ha Nguyen · 2020
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Dual consensus proximal algorithm for multi-agent sharing problems
Sulaiman A Alghunaim, Qi Lyu, Ming Yan, and Ali H Sayed · 2021
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Towards asynchronous federated learning for heterogeneous edge-powered internet of things
Zheyi Chen, Weixian Liao, Kun Hua, Chao Lu, and Wei Yu · 2021
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Blockchain-empowered decentralized horizontal federated learning for 5g-enabled uavs
Chaosheng Feng, Bin Liu, Keping Yu, Sotirios K Goudos, and Shaohua Wan · 2021
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Fast federated learning in the presence of arbitrary device unavailability
Xinran Gu, Kaixuan Huang, Jingzhao Zhang, and Longbo Huang · 2021
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Cafe: Catastrophic data leakage in vertical federated learning
Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, and Tianyi Chen · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
A unified analysis of federated learning with arbitrary client participation
Shiqiang Wang and Mingyue Ji · 2022
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Communication-efficient adaptive federated learning
Yujia Wang, Lu Lin, and Jinghui Chen · 2022
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Flexible vertical federated learning with heterogeneous parties
Timothy Castiglia, Shiqiang Wang, and Stacy Patterson · 2023
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A survey on decentralized federated learning
Edoardo Gabrielli, Giovanni Pica, and Gabriele Tolomei · 2023
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Starcraftimage: A dataset for prototyping spatial reasoning methods for multi-agent environments
Sean Kulinski, Nicholas R Waytowich, James Z Hare, and David I Inouye · 2023
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Fedvs: Straggler-resilient and privacy-preserving vertical federated learning for split models
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Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Semi-decentralized federated learning with cooperative d2d local model aggregations
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G Brinton, and Nicolo Michelusi · 2021
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Blockchain-enabled asynchronous federated learning in edge computing
Yinghui Liu, Youyang Qu, Chenhao Xu, Zhicheng Hao, and Bruce Gu · 2021
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Towards flexible device participation in federated learning
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Secure bilevel asynchronous vertical federated learning with backward updating
Qingsong Zhang, Bin Gu, Cheng Deng, and Heng Huang · 2021
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Compressed-vfl: Communication-efficient learning with vertically partitioned data
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Vf-ps: How to select important participants in vertical federated learning, efficiently and securely?
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Songze Li, Duanyi Yao, and Jin Liu · 2023
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Robust and ip-protecting vertical federated learning against unexpected quitting of parties
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A multi-token coordinate descent method for semi-decentralized vertical federated learning
Pedro Valdeira, Yuejie Chi, Cláudia Soares, and João Xavier · 2023
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A unified theory of diversity in ensemble learning
Danny Wood, Tingting Mu, Andrew M Webb, Henry WJ Reeve, Mikel Lujan, and Gavin Brown · 2023
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Decentralized federated learning: A survey and perspective
Liangqi Yuan, Lichao Sun, Philip S Yu, and Ziran Wang · 2023
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Federated learning with client subsampling, data heterogeneity, and unbounded smoothness: A new algorithm and lower bounds
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Vertical federated learning: Concepts, advances, and challenges
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A differentially private blockchain-based approach for vertical federated learning
Linh Tran, Sanjay Chari, Md Saikat Islam Khan, Aaron Zachariah, Stacy Patterson, and Oshani Seneviratne · 2024
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Vertical federated learning with missing features during training and inference
Pedro Valdeira, Shiqiang Wang, and Yuejie Chi · 2024
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