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Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., healthcare, financial, as well as scientific projects that lack a centralized data facility.
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Federated learning: Strategies for improving communication efficiency
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Decoupled weight decay regularization
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Communication-efficient learning of deep networks from decentralized data
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Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (isic)
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Local SGD converges fast and communicates little
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The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
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Towards federated learning at scale: System design
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Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation
Yang Chen, Xiaoyan Sun, and Yaochu Jin · 2019
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Bcn20000: Dermoscopic lesions in the wild
Marc Combalia, Noel CF Codella, Veronica Rotemberg, Brian Helba, Veronica Vilaplana, Ofer Reiter, Cristina Carrera, Alicia Barreiro, Allan C Halpern, Susana Puig, et al · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 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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Communication-efficient federated learning for wireless edge intelligence in IoT
Jed Mills, Jia Hu, and Geyong Min · 2019
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Client selection for federated learning with heterogeneous resources in mobile edge
Takayuki Nishio and Ryo Yonetani · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Interpret federated learning with shapley values
Guan Wang · 2019
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Measure contribution of participants in federated learning
Guan Wang, Charlie Xiaoqian Dang, and Ziye Zhou · 2019
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Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
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Federated machine learning: Concept and applications
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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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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End-to-end privacy preserving deep learning on multi-institutional medical imaging
Georgios Kaissis, Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Lima Jr, Jason Mancuso, Friederike Jungmann, Marc-Matthias Steinborn, et al · 2021
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TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Fernando Pérez-García, Rachel Sparks, and Sébastien Ourselin · 2021
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Secure aggregation for buffered asynchronous federated learning
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Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Parallel restarted with faster convergence and less communication: Demystifying why model averaging works for deep learning
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
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Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
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Asynchronous online federated learning for edge devices with non-iid data
Yujing Chen, Yue Ning, Martin Slawski, and Huzefa Rangwala · 2020
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Time efficient federated learning with semi-asynchronous communication
Jiangshan Hao, Yanchao Zhao, and Jiale Zhang · 2020
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Fedar: Activity and resource-aware federated learning model for distributed mobile robots
Ahmed Imteaj and M Hadi Amini · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
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Jinhyun So, Ramy E Ali, Başak Güler, and A Salman Avestimehr · 2021
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Efficient federated learning for fault diagnosis in industrial cloud-edge computing
Qizhao Wang, Qing Li, Kai Wang, Hong Wang, and Peng Zeng · 2021
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Asynchronous federated learning on heterogeneous devices: A survey
Chenhao Xu, Youyang Qu, Yong Xiang, and Longxiang Gao · 2021
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CSAFL: A clustered semi-asynchronous federated learning framework
Yu Zhang, Morning Duan, Duo Liu, Li Li, Ao Ren, Xianzhang Chen, Yujuan Tan, and Chengliang Wang · 2021
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Fedshuffle: Recipes for better use of local work in federated learning
Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, and Michael Rabbat · 2022
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A state-of-the-art survey on solving non-iid data in federated learning
Xiaodong Ma, Jia Zhu, Zhihao Lin, Shanxuan Chen, and Yangjie Qin · 2022
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Federated learning with buffered asynchronous aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, and Dzmitry Huba · 2022
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Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, et al · 2022
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Federated learning enables big data for rare cancer boundary detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards, Micah Sheller, Shih-Han Wang, G Anthony Reina, Patrick Foley, Alexey Gruzdev, Deepthi Karkada, Christos Davatzikos, et al · 2022
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Straggler-resilient federated learning: Leveraging the interplay between statistical accuracy and system heterogeneity
Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani, Aryan Mokhtari, and Ramtin Pedarsani · 2022
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APPFL: open-source software framework for privacy-preserving federated learning
Minseok Ryu, Youngdae Kim, Kibaek Kim, and Ravi K Madduri · 2022
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Trung-Hieu Hoang, Jordan Fuhrman, Ravi Madduri, Miao Li, Pranshu Chaturvedi, Zilinghan Li, Kibaek Kim, Minseok Ryu, Ryan Chard, EA Huerta, et al · 2023
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APPFLx: Providing privacy-preserving cross-silo federated learning as a service
Zilinghan Li, Shilan He, Pranshu Chaturvedi, Trung-Hieu Hoang, Minseok Ryu, EA Huerta, Volodymyr Kindratenko, Jordan Fuhrman, Maryellen Giger, Ryan Chard, et al · 2023
Closest in time.