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Federated learning usually employs a client-server architecture where an orchestrator iteratively aggregates model updates from remote clients and pushes them back a refined model.
“Peer-to-peer Federated Learning on Graphs”
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“Asymptotic Network Independence in Distributed Optimization for Machine Learning”, 2019
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“Advances and Open Problems in Federated Learning”, 2019
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“The max-plus algebra approach to railway timetable design”
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“Network Calculus: A Theory of Deterministic Queuing Systems for the Internet”
Jean-Yves Le and Patrick Thiran · 2001
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“End-to-End Available Bandwidth: Measurement Methodology, Dynamics, and Relation with TCP Throughput”
Manish Jain and Constantinos Dovrolis · 2002
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“Inferring Link Weights using End-to-End Measurements”
Ratul Mahajan, Neil Spring, David Wetherall and Tom Anderson · 2002
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“Approximation algorithms for the traveling salesman problem”
Jérôme Monnot, Vangelis. Paschos and Sophie Toulouse · 2002
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“Is Local SGD Better than Minibatch SGD?”, 2020
Blake Woodworth et al · 2002
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“Automated control synthesis for an assembly line using discrete event system control theory”
Vigyan Chandra, Zhongdong Huang and Ratnesh Kumar · 2003
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“A Unified Theory of Decentralized SGD with Changing Topology and Local Updates”, 2020
Anastasia Koloskova et al · 2003
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“Fast linear iterations for distributed averaging”
Lin Xiao and S. Boyd · 2003
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“Bandwidth estimation: metrics, measurement techniques, and tools”
R. Prasad, C. Dovrolis, M. Murray and K. Claffy · 2003
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“Measuring ISP Topologies with Rocketfuel”
Neil Spring, Ratul Mahajan, David Wetherall and Thomas Anderson · 2003
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“Constraint-Based Geolocation of Internet Hosts”
Bamba Gueye, Artur Ziviani, Mark Crovella and Serge Fdida · 2004
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“Providing and verifying advanced IP services in hierarchical DiffServ networks-the case of GEANT”
Athanassios Liakopoulos, Basil Maglaris, Christos Bouras and Afrodite Sevasti · 2004
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“The traveling salesman problem and its variations”
Gregory Gutin and Abraham Punnen · 2006
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“The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)”
David. Applegate, Robert. Bixby, Vasek Chvatal and William. Cook · 2007
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“On variants of shortest-path betweenness centrality and their generic computation”
Ulrik Brandes · 2007
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“Randomized Decentralized Broadcasting Algorithms”
L. Massoulie, A. Twigg, C. Gkantsidis and P. Rodriguez · 2007
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“Exploring Network Structure, Dynamics, and Function using NetworkX”
Aric. Hagberg, Daniel. Schult and Pieter. Swart · 2008
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“ImageNet: A Large-Scale Hierarchical Image Database”
J. Deng et al · 2009
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“Twitter Sentiment Classification using Distant Supervision”
Alec Go, Richa Bhayani and Lei Huang · 2009
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“Distributed Subgradient Methods for Multi-Agent Optimization”
Angelia Nedić and Asuman. Ozdaglar · 2009
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“Modeling and control of high-throughput screening systems” Special Section: IFAC Conference on Analysis and Design of Hybrid Systems (ADHS’09) in Zaragoza, Spain, 16th-18th September, 2009
T. Brunsch, J. Raisch and L. Hardouin · 2010
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“The traffic phases of road networks”
N. Farhi, M. Goursat and J.-P. Quadrat · 2010
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“Torchvision the Machine-Vision Package of Torch”
Sébastien Marcel and Yann Rodriguez · 2010
“ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning”
Hantian Zhang et al · 2017
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“Personalized and Private Peer-to-Peer Machine Learning”
Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki and Marc Tommasi · 2018
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“Expanding the reach of federated learning by reducing client resource requirements”
Sebastian Caldas, Jakub Konečny, H McMahan and Ameet Talwalkar · 2018
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“LEAF: A Benchmark for Federated Settings”, 2018
Sebastian Caldas et al · 2018
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“Moving Bits with a Fleet of Shared Virtual Routers”
P. Kathiravelu et al · 2018
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Cited alongside, same era.
“A new class of distributed optimization algorithms: application to regression of distributed data”
S. Ram, Angelia Nedic and Venugopal. Veeravalli · 2010
Cited alongside, same era.
“Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling”
J.. Duchi, A. Agarwal and M.. Wainwright · 2011
Cited alongside, same era.
“The Internet Topology Zoo”
S. Knight et al · 2011
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“Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning”
K.. Tsianos, S. Lawlor and M.. Rabbat · 2012
Cited alongside, same era.
“Privacy-preserving ridge regression on hundreds of millions of records”
Valeria Nikolaenko et al · 2013
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“On the Properties of Neural Machine Translation: Encoder-Decoder Approaches”
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau and Yoshua Bengio · 2014
Cited alongside, same era.
“Near-Optimal Straggler Mitigation for Distributed Gradient Methods”
Songze Li, Seyed Kalan, A. Avestimehr and Mahdi Soltanolkotabi · 2018
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“Asynchronous Decentralized Parallel Stochastic Gradient Descent”
Xiangru Lian, Wei Zhang, Ce Zhang and Ji Liu · 2018
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“Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization”
A. Nedić, A. Olshevsky and M.. Rabbat · 2018
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“Optimal algorithms for non-smooth distributed optimization in networks”
Kevin Scaman et al · 2018
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“ D 2 D^{2} : Decentralized Training over Decentralized Data”
Hanlin Tang et al · 2018
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“The iNaturalist Species Classification and Detection Dataset”
G. Van Horn et al · 2018
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“Atomo: Communication-efficient learning via atomic sparsification”
Hongyi Wang et al · 2018
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“Algorithms for Euclidean Degree Bounded Spanning Tree Problems”
Patrick. Andersen and Charl. Ras · 2019
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“Stochastic Gradient Push for Distributed Deep Learning”
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas and Michael Rabbat · 2019
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“Towards Federated Learning at Scale: System Design”
Keith Bonawitz et al · 2019
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“Deep learning-based classification of mesothelioma improves prediction of patient outcome”
Pierre Courtiol et al · 2019
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“Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication”
Anastasia Koloskova, Sebastian Stich and Martin Jaggi · 2019
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“Communication-Efficient Local Decentralized SGD Methods.”
Xiang Li, Wenhao Yang, Shusen Wang and Zhihua Zhang · 2019
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“Hop: Heterogeneity-Aware Decentralized Training”
Qinyi Luo, Jinkun Lin, Youwei Zhuo and Xuehai Qian · 2019
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URL: https://www.imi.europa.eu/projects-results/project-factsheets/melloddy
“Machine learning ledger orchestration for drug discovery (MELLODY)” EU research project, 2019 · 2019
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In Musketeer: About, 2019
“Musketeer” · 2019
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“The Role of Network Topology for Distributed Machine Learning”
G. Neglia, G. Calbi, D. Towsley and G. Vardoyan · 2019
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“Robust and communication-efficient federated learning from non-iid data”
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller and Wojciech Samek · 2019
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“Federated learning in distributed medical databases: Meta-analysis of large-scale subcortical brain data”
Santiago Silva et al · 2019
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“Federated Learning over Wireless Networks: Optimization Model Design and Analysis”
N.. Tran et al · 2019
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“Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD”
Jianyu Wang and Gauri Joshi · 2019
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“Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms”
Jianyu Wang and Gauri Joshi · 2019
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“MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling”
Jianyu Wang et al · 2019
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“Adaptive Federated Learning in Resource Constrained Edge Computing Systems”
Shiqiang Wang et al · 2019
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“Exact Diffusion for Distributed Optimization and Learning—Part I: Algorithm Development”
K. Yuan, B. Ying, X. Zhao and A.. Sayed · 2019
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“Federated Learning: Challenges, Methods, and Future Directions”
Tian Li, Anit Sahu, Ameet Talwalkar and Virginia Smith · 2020
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“Federated Optimization in Heterogeneous Networks”
Tian Li et al · 2020
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“Don’t Use Large Mini-batches, Use Local SGD”
Tao Lin, Sebastian. Stich, Kumar Patel and Martin Jaggi · 2020
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URL: https://blogs.nvidia.com/blog/2020/04/15/federated-learning-mammogram-assessment/
“Mammogram Assessment with NVIDIA Clara Federated Learning” EU research project, 2020 · 2020
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“Decentralized gradient methods: does topology matter?”
Giovanni Neglia, Chuan Xu, Don Towsley and Gianmarco Calbi · 2020
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“Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning: Examining Distributed and Centralized Stochastic Gradient Descent”
Shi Pu, Alex Olshevsky and Ioannis. Paschalidis · 2020
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