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The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machine learning models.
“Variational Federated Multi-Task Learning”, 2019
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“Federated Learning of a Mixture of Global and Local Models”, 2020
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“Unlabeled Data Does Provably Help”
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“EMNIST: Extending MNIST to handwritten letters”
Gregory Cohen, Saeed Afshar, Jonathan Tapson and Andre Van · 2017
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“Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent”
Xiangru Lian et al · 2017
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“Communication-efficient learning of deep networks from decentralized data”
Brendan McMahan et al · 2017
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“Federated Multi-Task Learning”
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi and Ameet Talwalkar · 2017
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“Decentralized Collaborative Learning of Personalized Models over Networks”
Paul Vanhaesebrouck, Aurélien Bellet and Marc Tommasi · 2017
Cited alongside, same era.
“Personalized and Private Peer-to-Peer Machine Learning”
Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki and Marc Tommasi · 2018
“SCAFFOLD: Stochastic controlled averaging for federated learning”
Sai Karimireddy et al · 2020
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“A Unified Theory of Decentralized SGD with Changing Topology and Local Updates”
Anastasia Koloskova et al · 2020
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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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“FedBN: Federated Learning on Non-IID Features via Local Batch Normalization”
Xiaoxiao Li et al · 2020
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“Three approaches for personalization with applications to federated learning”
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Cited alongside, same era.
Sebastian Caldas et al · 2018
Cited alongside, same era.
“Asynchronous Decentralized Parallel Stochastic Gradient Descent”
Xiangru Lian, Wei Zhang, Ce Zhang and Ji Liu · 2018
Cited alongside, same era.
“Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization”
A. Nedić, A. Olshevsky and M.. Rabbat · 2018
Cited alongside, same era.
“Mobilenetv2: Inverted residuals and linear bottlenecks”
Mark Sandler et al · 2018
Cited alongside, same era.
“Local SGD Converges Fast and Communicates Little”
Sebastian Stich · 2018
Cited alongside, same era.
“ D 2 D^{2} : Decentralized Training over Decentralized Data”
Hanlin Tang et al · 2018
Cited alongside, same era.
Yishay Mansour, Mehryar Mohri, Jae Ro and Ananda Suresh · 2020
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“Throughput-Optimal Topology Design for Cross-Silo Federated Learning”
Othmane Marfoq, Chuan Xu, Giovanni Neglia and Richard Vidal · 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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“Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints”
Felix Sattler, Klaus-Robert Müller and Wojciech Samek · 2020
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“Weighted Emprirical Risk Minimization: Transfer Learning based on Importance Sampling”
Robin Vogel, Mastane Achab, Stéphan Clémençon and Charles Tillier · 2020
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“Federated Learning with Matched Averaging”
Hongyi Wang et al · 2020
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“Tackling the objective inconsistency problem in heterogeneous federated optimization”
Jianyu Wang et al · 2020
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“Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs” 108
Valentina Zantedeschi, Aurélien Bellet and Marc Tommasi · 2020
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“Personalized Federated Learning with First Order Model Optimization”
Michael Zhang et al · 2020
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“Debiasing Model Updates for Improving Personalized Federated Training”
Durmus Acar et al · 2021
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“Federated Expectation Maximization with heterogeneity mitigation and variance reduction”
Aymeric Dieuleveut, Gersende Fort, Eric Moulines and Geneviève Robin · 2021
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“FedU: A Unified Framework for Federated Multi-Task Learning with Laplacian Regularization”
Canh Dinh et al · 2021
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“Federated Learning with Compression: Unified Analysis and Sharp Guarantees”
Farzin Haddadpour, Mohammad Kamani, Aryan Mokhtari and Mehrdad Mahdavi · 2021
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“Personalized cross-silo federated learning on non-iid data”
Yutao Huang et al · 2021
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“Advances and Open Problems in Federated Learning”
Peter Kairouz et al · 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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“Adaptive Federated Optimization”
Sashank. Reddi et al · 2021
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“Personalized Federated Learning using Hypernetworks”
Aviv Shamsian, Aviv Navon, Ethan Fetaya and Gal Chechik · 2021
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