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Federated Learning (FL) distributes machine learning (ML) training across edge devices to reduce data transfer overhead and protect data privacy.
Machine Learning: Trends, Perspectives, and Prospects
M. I. Jordan and T. M. Mitchell · 2015
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Regulation (EU) 2016/679 of the European Parliament and of the Council, 2016
European Parliament and Council of the European Union · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
EMNIST: Extending MNIST to Handwritten Letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
Earlier work this paper cites.
Differentially Private Federated Learning: A Client-Level Perspective
Robin C. Geyer, Tassilo Klein, and Moin Nabi · 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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Don’t Decay the Learning Rate, Increase the Batch Size
Samuel L Smith, Pieter-Jan Kindermans, Chris Ying, and Quoc V Le · 2017
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Leaf: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Earlier work this paper cites.
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Sebastian Caldas, Jakub Konecny, Brendan McMahan, and Ameet Talwalkar · 2018
Earlier work this paper cites.
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Tianyi Chen, Georgios Giannakis, Tao Sun, and Wotao Yin · 2018
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Training Deep Models Faster with Robust, Approximate Importance Sampling
Tyler B. Johnson and Carlos Guestrin · 2018
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Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Angelos Katharopoulos and Francois Fleuret · 2018
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Ray: A Distributed Framework for Emerging AI Applications, 2018
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E. Gonzalez, and Ion Stoica · 2018
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Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
Pete Warden · 2018
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Applied Federated Learning: Improving Google Keyboard Query Suggestions, 2018
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
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Critical Learning Periods in Deep Networks
Alessandro Achille, Matteo Rovere, and Stefano Soatto · 2019
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Towards Federated Learning at Scale: System Design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
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Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
Aditya Sharad Golatkar, Alessandro Achille, and Stefano Soatto · 2019
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On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
Stanislaw Jastrzebski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amos J. Storkey · 2019
Earlier work this paper cites.
Privacy Should Not Be a Luxury Good, 2019
Sundar Pichai · 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.
Efficient Replication for Straggler Mitigation in Distributed Computing
Amir Behrouzi-Far and Emina Soljanin · 2020
Cited alongside, same era.
Flower: A Friendly Federated Learning Research Framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Hei Li Kwing, Titouan Parcollet, Pedro PB de Gusmão, and Nicholas D Lane · 2020
Cited alongside, same era.
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
Canh T Dinh, Nguyen H Tran, Minh NH Nguyen, Choong Seon Hong, Wei Bao, Albert Y Zomaya, and Vincent Gramoli · 2020
Cited alongside, same era.
PAPAYA: Practical, Private, and Scalable Federated Learning
Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, Kaikai Wang, Anthony Shoumikhin, Jesik Min, and Mani Malek · 2022
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PyramidFL: A Fine-Grained Client Selection Framework for Efficient Federated Learning
Chenning Li, Xiao Zeng, Mi Zhang, and Zhichao Cao · 2022
Later among the works it cites.
Electricity Map
Electricity Maps · 2022
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Fedbalancer: Data and pace control for efficient federated learning on heterogeneous clients
Jaemin Shin, Yuanchun Li, Yunxin Liu, and Sung-Ju Lee · 2022
Later among the works it cites.
Sustainable AI: Environmental Implications, Challenges and Opportunities
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, and Kim Hazelwood · 2022
Later among the works it cites.
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Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong · 2020
Cited alongside, same era.
The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B. Gibbons · 2020
Cited alongside, same era.
Scaffold: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Federated Optimization in Heterogeneous Networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Fair Resource Allocation in Federated Learning
Tian Li, Manzil Zaheer, Ahmad Beirami, and Virginia Smith · 2020
Cited alongside, same era.
FairFL: A Fair Federated Learning Approach to Reducing Demographic Bias in Privacy-Sensitive Classification Models
Daniel Yue Zhang, Ziyi Kou, and Dong Wang · 2020
Cited alongside, same era.
Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification
Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, and Dimitris Papailiopoulos · 2021
Cited alongside, same era.
REFL: Resource-Efficient Federated Learning
Ahmed M. Abdelmoniem, Atal Narayan Sahu, Marco Canini, and Suhaib A. Fahmy · 2023
Closest in time.
Diloco: Distributed Low-Communication Training of Language Models
Arthur Douillard, Qixuan Feng, Andrei A Rusu, Rachita Chhaparia, Yani Donchev, Adhiguna Kuncoro, Marc’Aurelio Ranzato, Arthur Szlam, and Jiajun Shen · 2023
Closest in time.
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
Lei Fu, Huanle Zhang, Ge Gao, Mi Zhang, and Xin Liu · 2023
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A First Look into the Carbon Footprint of Federated Learning
Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro PB Gusmao, Yan Gao, Daniel J Beutel, Taner Topal, Akhil Mathur, and Nicholas D Lane · 2023
Closest in time.
CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning
Gang Yan, Hao Wang, Xu Yuan, and Jian Li · 2023
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DeFL: Defending Against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness
Gang Yan, Hao Wang, Xu Yuan, and Jian Li · 2023
Closest in time.
Green Federated Learning
Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael Rabbat, Carole-Jean Wu, and Ilya Mironov · 2023
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CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
Jieming Bian, Lei Wang, Shaolei Ren, and Jie Xu · 2024
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Google Data Centers: Efficiency, 2024
Google · 2024
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Photon: Federated LLM Pre-Training
Lorenzo Sani, Alex Iacob, Zeyu Cao, Royson Lee, Bill Marino, Yan Gao, Dongqi Cai, Zexi Li, Wanru Zhao, Xinchi Qiu, et al · 2024
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The Future of Large Language Model Pre-training is Federated
Lorenzo Sani, Alex Iacob, Zeyu Cao, Bill Marino, Yan Gao, Tomas Paulik, Wanru Zhao, William F Shen, Preslav Aleksandrov, and Xinchi Qiu · 2024
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ML Training with Cloud GPU Shortages: Is Cross-Region the Answer?
Foteini Strati, Paul Elvinger, Tolga Kerimoglu, and Ana Klimovic · 2024
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Will We Run Out of Data? Limits of LLM Scaling Based on Human-Generated Data
Pablo Villalobos, Anson Ho, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, and Marius Hobbhahn · 2024
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Fedzero: Leveraging Renewable Excess Energy in Federated Learning
Philipp Wiesner, Ramin Khalili, Dennis Grinwald, Pratik Agrawal, Lauritz Thamsen, and Odej Kao · 2024
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