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Federated learning (FL) is a rapidly growing research field in machine learning.
Gradient-based learning applied to document recognition
LeCun, Y., L. Bottou, Y. Bengio, et al · 1998
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Pachinko allocation: Dag-structured mixture models of topic correlations
Li, W., A. McCallum · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A., G. Hinton, et al · 2009
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NUS-WIDE: A real-world web image database from National University of Singapore
Chua, T.-S., J. Tang, R. Hong, et al · 2009
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Chen, T., M. Li, Y. Li, et al · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., S. Jha, T. Ristenpart · 2015
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Practical secure aggregation for federated learning on user-held data
Bonawitz, K., V. Ivanov, B. Kreuter, et al · 2016
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Lin, Y., S. Han, H. Mao, et al · 2017
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Deep models under the gan: information leakage from collaborative deep learning
Hitaj, B., G. Ateniese, F. Perez-Cruz · 2017
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Differentially private federated learning: A client level perspective
Geyer, R. C., T. Klein, M. Nabi · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., E. Moore, D. Ramage, et al · 2017
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Hardy, S., W. Henecka, H. Ivey-Law, et al · 2017
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Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Lian, X., C. Zhang, H. Zhang, et al · 2017
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Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K., V. Ivanov, B. Kreuter, et al · 2017
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Machine learning with adversaries: Byzantine tolerant gradient descent
Blanchard, P., R. Guerraoui, J. Stainer, et al · 2017
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Hardy, S., W. Henecka, H. Ivey-Law, et al · 2017
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Emnist: an extension of mnist to handwritten letters. arxiv e-prints
Cohen, G., S. Afshar, J. Tapson, et al · 2017
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On the convergence of federated optimization in heterogeneous networks
Sahu, A. K., T. Li, M. Sanjabi, et al · 2018
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Communication compression for decentralized training
Tang, H., S. Gan, C. Zhang, et al · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Yin, D., Y. Chen, K. Ramchandran, et al · 2018
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Mitigating sybils in federated learning poisoning
Fung, C., C. J. Yoon, I. Beschastnikh · 2018
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Gradient-leaks: Understanding and controlling deanonymization in federated learning
Orekondy, T., S. J. Oh, Y. Zhang, et al · 2018
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A generic framework for privacy preserving deep learning
Ryffel, T., A. Trask, M. Dahl, et al · 2018
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Horovod: fast and easy distributed deep learning in tensorflow
Sergeev, A., M. Del Balso · 2018
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Leaf: A benchmark for federated settings
Caldas, S., P. Wu, T. Li, et al · 2018
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Entity resolution and federated learning get a federated resolution
Nock, R., S. Hardy, W. Henecka, et al · 2018
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Distributed learning of deep neural network over multiple agents
Gupta, O., R. Raskar · 2018
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Split learning for health: Distributed deep learning without sharing raw patient data
Vepakomma, P., O. Gupta, T. Swedish, et al · 2018
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Jeong, E., S. Oh, H. Kim, et al · 2018
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Federated learning for mobile keyboard prediction
Hard, A., K. Rao, R. Mathews, et al · 2018
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Fadl: Federated-autonomous deep learning for distributed electronic health record
Liu, D., T. Miller, R. Sayeed, et al · 2018
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Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation
Sheller, M. J., G. A. Reina, B. Edwards, et al · 2018
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Federated optimization in heterogeneous networks
Li, T., A. K. Sahu, M. Zaheer, et al · 2018
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Cinic-10 is not imagenet or cifar-10
Darlow, L. N., E. J. Crowley, A. Antoniou, et al · 2018
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Advances and open problems in federated learning
Kairouz, P., H. B. McMahan, B. Avent, et al · 2019
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Deepsqueeze: Decentralization meets error-compensated compression
Tang, H., X. Lian, S. Qiu, et al · 2019
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Deep leakage from gradients
Zhu, L., Z. Liu, S. Han · 2019
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Nasr, M., R. Shokri, A. Houmansadr · 2019
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Wang, Z., M. Song, Z. Zhang, et al · 2019
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Analyzing federated learning through an adversarial lens
Bhagoji, A. N., S. Chakraborty, P. Mittal, et al · 2019
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Can you really backdoor federated learning?
Sun, Z., P. Kairouz, A. T. Suresh, et al · 2019
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Exploiting unintended feature leakage in collaborative learning
Melis, L., C. Song, E. De Cristofaro, et al · 2019
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A hybrid approach to privacy-preserving federated learning
Truex, S., N. Baracaldo, A. Anwar, et al · 2019
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Federated learning with bayesian differential privacy
Triastcyn, A., B. Faltings · 2019
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Hybridalpha: An efficient approach for privacy-preserving federated learning
Xu, R., N. Baracaldo, Y. Zhou, et al · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., S. Gross, F. Massa, et al · 2019
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A generic communication scheduler for distributed dnn training acceleration
Peng, Y., Y. Zhu, Y. Chen, et al · 2019
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TensorFlow Federated , 2019
Ingerman, A., K. Ostrowski · 2019
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Federated learning
Yang, Q., Y. Liu, Y. Cheng, et al · 2019
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Paddlepaddle: An open-source deep learning platform from industrial practice
Ma, Y., D. Yu, T. Wu, et al · 2019
Cited alongside, same era.
Secureboost: A lossless federated learning framework
Cheng, K., T. Fan, Y. Jin, et al · 2019
Cited alongside, same era.
Yang, S., B. Ren, X. Zhou, et al · 2019
Cited alongside, same era.
A quasi-newton method based vertical federated learning framework for logistic regression
Yang, K., T. Fan, T. Chen, et al · 2019
Cited alongside, same era.
Central server free federated learning over single-sided trust social networks
An overview of federated deep learning privacy attacks and defensive strategies
Enthoven, D., Z. Al-Ars · 2020
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Federated generative privacy
Triastcyn, A., B. Faltings · 2020
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Multi-participant multi-class vertical federated learning
Feng, H., Siwei Yu · 2020
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Asymmetrically vertical federated learning
Liu, Y., X. Zhang, L. Wang · 2020
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Multi-consensus decentralized accelerated gradient descent
Ye, H., L. Luo, Z. Zhou, et al · 2020
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He, C., C. Tan, H. Tang, et al · 2019
Cited alongside, same era.
Decentralized bayesian learning over graphs
Lalitha, A., X. Wang, O. Kilinc, et al · 2019
Cited alongside, same era.
Federated hierarchical hybrid networks for clickbait detection
Liao, F., H. H. Zhuo, X. Huang, et al · 2019
Cited alongside, same era.
Client-edge-cloud hierarchical federated learning
Liu, L., J. Zhang, S. Song, et al · 2019
Cited alongside, same era.
Improving federated learning personalization via model agnostic meta learning
Jiang, Y., J. Konečnỳ, K. Rush, et al · 2019
Cited alongside, same era.
Adaptive gradient-based meta-learning methods
Khodak, M., M.-F. F. Balcan, A. S. Talwalkar · 2019
Cited alongside, same era.
Md-gan: Multi-discriminator generative adversarial networks for distributed datasets
Hardy, C., E. Le Merrer, B. Sericola · 2019
Cited alongside, same era.
Generative models for effective ml on private, decentralized datasets
Augenstein, S., H. B. McMahan, D. Ramage, et al · 2019
Cited alongside, same era.
Wainakh, A., A. S. Guinea, T. Grube, et al · 2020
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Federated learning with hierarchical clustering of local updates to improve training on non-iid data
Briggs, C., Z. Fan, P. Andras · 2020
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Hierarchical federated learning across heterogeneous cellular networks
Abad, M. S. H., E. Ozfatura, D. Gunduz, et al · 2020
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Luo, S., X. Chen, Q. Wu, et al · 2020
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Personalized federated learning: A meta-learning approach
Fallah, A., A. Mokhtari, A. Ozdaglar · 2020
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Federated semi-supervised learning with inter-client consistency
Jeong, W., J. Yoon, E. Yang, et al · 2020
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Fednas: Federated deep learning via neural architecture search
He, C., M. Annavaram, S. Avestimehr · 2020
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Differentially-private federated neural architecture search
Singh, I., H. Zhou, K. Yang, et al · 2020
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Neural architecture search over decentralized data
Xu, M., Y. Zhao, K. Bian, et al · 2020
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A secure federated transfer learning framework
qiang Liu, Y., Y. Kang, C. Xing, et al · 2020
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Federated visual classification with real-world data distribution
Hsu, T.-M. H., H. Qi, M. Brown · 2020
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Fedvision: An online visual object detection platform powered by federated learning
Liu, Y., A. Huang, Y. Luo, et al · 2020
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Fedner: Medical named entity recognition with federated learning
Ge, S., F. Wu, C. Wu, et al · 2020
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Federated pretraining and fine tuning of bert using clinical notes from multiple silos
Liu, D., T. Miller · 2020
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Fedcoin: A peer-to-peer payment system for federated learning
Liu, Y., S. Sun, Z. Ai, et al · 2020
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Federated learning for vehicular networks
Elbir, A. M., S. Coleri · 2020
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Lim, W. Y. B., J. Huang, Z. Xiong, et al · 2020
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Federated learning meets contract theory: Energy-efficient framework for electric vehicle networks
Saputra, Y. M., D. N. Nguyen, D. T. Hoang, et al · 2020
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Privacy-preserving traffic flow prediction: A federated learning approach
Liu, Y., J. James, J. Kang, et al · 2020
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Privacy in deep learning: A survey
Mirshghallah, F., M. Taram, P. Vepakomma, et al · 2020
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Yin, F., Z. Lin, Y. Xu, et al · 2020
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Practical privacy preserving poi recommendation
Chen, C., B. Wu, W. Fang, et al · 2020
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The future of digital health with federated learning
Rieke, N., J. Hancox, W. Li, et al · 2020
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Ju, C., R. Zhao, J. Sun, et al · 2020
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Federated transfer learning for eeg signal classification
Ju, C., D. Gao, R. Mane, et al · 2020
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Fedhealth: A federated transfer learning framework for wearable healthcare
Chen, Y., X. Qin, J. Wang, et al · 2020
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Federated multi-view matrix factorization for personalized recommendations
Flanagan, A., W. Oyomno, A. Grigorievskiy, et al · 2020
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Robust federated recommendation system
Chen, C., J. Zhang, A. K. Tung, et al · 2020
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Federated recommendation system via differential privacy
Li, T., L. Song, C. Fragouli · 2020
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Fedrec: Privacy-preserving news recommendation with federated learning
Qi, T., F. Wu, C. Wu, et al · 2020
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Federating recommendations using differentially private prototypes
Ribero, M., J. Henderson, S. Williamson, et al · 2020
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Cloud-based federated boosting for mobile crowdsensing
Wang, Z., Y. Yang, Y. Liu, et al · 2020
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Exploiting unlabeled data in smart cities using federated learning
Albaseer, A., B. S. Ciftler, M. Abdallah, et al · 2020
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Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
So, J., B. Guler, A. S. Avestimehr · 2020
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Attack of the tails: Yes, you really can backdoor federated learning
Wang, H., K. Sreenivasan, S. Rajput, et al · 2020
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Group knowledge transfer: Collaborative training of large cnns on the edge
He, C., S. Avestimehr, M. Annavaram · 2020
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Backdoor attacks and defenses in feature-partitioned collaborative learning
Liu, Y., Z. Yi, T. Chen · 2020
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Communication-efficient multimodal split learning for mmwave received power prediction
Koda, Y., J. Park, M. Bennis, et al · 2020
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Milenas: Efficient neural architecture search via mixed-level reformulation
He, C., H. Ye, L. Shen, et al · 2020
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Fetchsgd: Communication-efficient federated learning with sketching
Rothchild, D., A. Panda, E. Ullah, et al · 2020
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Federated learning with only positive labels
Yu, F. X., A. S. Rawat, A. K. Menon, et al · 2020
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From local sgd to local fixed point methods for federated learning
Malinovsky, G., D. Kovalev, E. Gasanov, et al · 2020
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Acceleration for compressed gradient descent in distributed and federated optimization
Li, Z., D. Kovalev, X. Qian, et al · 2020
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