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Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Semantic contours from inverse detectors
Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik · 2011
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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U-net: Convolutional networks for biomedical image segmentation, 2015
Thomas Brox Olaf Ronneberger, Philipp Fischer · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Practical secure aggregation for federated learning on user-held data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2016
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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 · 2016
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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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Mitigating sybils in federated learning poisoning
Clement Fung, Chris JM Yoon, and Ivan Beschastnikh · 2018
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Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
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Gradient-leaks: Understanding and controlling deanonymization in federated learning
Tribhuvanesh Orekondy, Seong Joon Oh, Yang Zhang, Bernt Schiele, and Mario Fritz · 2018
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A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, and Jonathan Passerat-Palmbach · 2018
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On the convergence of federated optimization in heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 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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Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
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Autoaugment: Learning augmentation policies from data
Ekin Dogus Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2019
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Searching for mobilenetv3
A. Howard, M. Sandler, B. Chen, W. Wang, L. Chen, M. Tan, G. Chu, V. Vasudevan, Y. Zhu, R. Pang, H. Adam, and Q. Le · 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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Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2019
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Privacy-preserving Federated Brain Tumour Segmentation
Wenqi Li, Fausto Milletarì, Daguang Xu, Nicola Rieke, Jonny Hancox, Wentao Zhu, Maximilian Baust, Yan Cheng, Sébastien Ourselin, M. Jorge Cardoso, and Andrew Feng · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Federated learning with bayesian differential privacy
Aleksei Triastcyn and Boi Faltings · 2019
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A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
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Measuring the effects of non-identical data distribution for federated visual classification, 2019
Matthew Brown Tzu-Ming Harry Hsu, Hang Qi · 2019
Cited alongside, same era.
Beyond inferring class representatives: User-level privacy leakage from federated learning
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi · 2019
Cited alongside, same era.
Dba: Distributed backdoor attacks against federated learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li · 2019
Cited alongside, same era.
Hybridalpha: An efficient approach for privacy-preserving federated learning
Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, and Heiko Ludwig · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Cited alongside, same era.
Federated object detection: Optimizing object detection model with federated learning
Fetchsgd: Communication-efficient federated learning with sketching
Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora · 2020
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Byzantine-resilient secure federated learning
Jinhyun So, Basak Guler, and A. Salman Avestimehr · 2020
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Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
Jinhyun So, Basak Guler, and A Salman Avestimehr · 2020
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Federated generative privacy
Aleksei Triastcyn and Boi Faltings · 2020
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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Peihua Yu and Yunfeng Liu · 2019
Cited alongside, same era.
Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni · 2019
Cited alongside, same era.
How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
Cited alongside, same era.
Yolov4: Optimal speed and accuracy of object detection
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao · 2020
Cited alongside, same era.
Qi Chang, Hui Qu, Yikai Zhang, Mert Sabuncu, Chao Chen, Tong Zhang, and Dimitris Metaxas · 2020
Cited alongside, same era.
Backdoor attacks on federated meta-learning
Chien-Lun Chen, Leana Golubchik, and Marco Paolieri · 2020
Cited alongside, same era.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
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A framework for evaluating gradient leakage attacks in federated learning
Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu · 2020
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Google landmarks dataset v2 – a large-scale benchmark for instance-level recognition and retrieval
T. Weyand, A. Araujo, B. Cao, and J. Sim · 2020
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Federated learning with only positive labels
Felix X Yu, Ankit Singh Rawat, Aditya Krishna Menon, and Sanjiv Kumar · 2020
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Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Fairfed: Enabling group fairness in federated learning
Yahya H Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, and Salman Avestimehr · 2021
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Personalized federated learning: A unified framework and universal optimization techniques
Filip Hanzely, Boxin Zhao, and Mladen Kolar · 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, et al · 2021
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Spreadgnn: Serverless multi-task federated learning for graph neural networks
Chaoyang He, Emir Ceyani, Keshav Balasubramanian, Murali Annavaram, and Salman Avestimehr · 2021
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Pipetransformer: Automated elastic pipelining for distributed training of large-scale models
Chaoyang He, Shen Li, Mahdi Soltanolkotabi, and Salman Avestimehr · 2021
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Ssfl: Tackling label deficiency in federated learning via personalized self-supervision
Chaoyang He, Zhengyu Yang, Erum Mushtaq, Sunwoo Lee, Mahdi Soltanolkotabi, and Salman Avestimehr · 2021
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Lightweight image super-resolution with hierarchical and differentiable neural architecture search
Han Huang, Li Shen, Chaoyang He, Weisheng Dong, Haozhi Huang, and Guangming Shi · 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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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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Layer-wise adaptive model aggregation for scalable federated learning
Sunwoo Lee, Tuo Zhang, Chaoyang He, and Salman Avestimehr · 2021
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Omnilytics: A blockchain-based secure data market for decentralized machine learning
Jiacheng Liang, Songze Li, Wensi Jiang, Bochuan Cao, and Chaoyang He · 2021
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Fednlp: A research platform for federated learning in natural language processing
Bill Yuchen Lin, Chaoyang He, Zihang Zeng, Hulin Wang, Yufen Huang, Mahdi Soltanolkotabi, Xiang Ren, and Salman Avestimehr · 2021
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Federated reconstruction: Partially local federated learning
Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, and Sushant Prakash · 2021
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A field guide to federated optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al · 2021
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Lightsecagg: Rethinking secure aggregation in federated learning
Chien-Sheng Yang, Jinhyun So, Chaoyang He, Songze Li, Qian Yu, and Salman Avestimehr · 2021
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Mest: Accurate and fast memory-economic sparse training framework on the edge
Geng Yuan, Xiaolong Ma, Wei Niu, Zhengang Li, Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, et al · 2021
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Federated learning for internet of things: Applications, challenges, and opportunities
Tuo Zhang, Lei Gao, Chaoyang He, Mi Zhang, Bhaskar Krishnamachari, and Salman Avestimehr · 2021
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Federated learning for internet of things
Tuo Zhang, Chaoyang He, Tianhao Ma, Lei Gao, Mark Ma, and Amir Salman Avestimehr · 2021
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