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Federated learning is the centralized training of statistical models from decentralized data on mobile devices while preserving the privacy of each device.
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Robust Estimators in High Dimensions without the Computational Intractability
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Loss Minimization and Parameter Estimation with Heavy Tails
D. J. Hsu and S. Sabato · 2016
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Towards Federated Learning at Scale: System Design
K. A. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konečný, S. Mazzocchi, B. McMahan, T. V. Overveldt, D. Petrou, D. Ramage, and J. Roselander · 2019
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Distributed Gradient Descent Algorithm Robust to an Arbitrary Number of Byzantine Attackers
X. Cao and L. Lai · 2019
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Resilient Distributed Parameter Estimation With Heterogeneous Data
Y. Chen, S. Kar, and J. M. Moura · 2019
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High-Dimensional Robust Mean Estimation in Nearly-Linear Time
Y. Cheng, I. Diakonikolas, and R. Ge · 2019
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Patient Clustering Improves Efficiency of Federated Machine Learning to Predict Mortality and Hospital stay time using Distributed Electronic Medical Records
L. Huang, A. L. Shea, H. Qian, A. Masurkar, H. Deng, and D. Liu · 2019
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Robust Distributed Estimation by Networked Agents
S. Al-Sayed, A. M. Zoubir, and A. H. Sayed · 2017
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Machine learning with adversaries: Byzantine tolerant gradient descent
P. Blanchard, R. Guerraoui, E. M. El Mhamdi, and J. Stainer · 2017
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Practical Secure Aggregation for Privacy-Preserving Machine Learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2017
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Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent
Y. Chen, L. Su, and J. Xu · 2017
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EMNIST: an extension of MNIST to handwritten letters
G. Cohen, S. Afshar, J. Tapson, and A. van Schaik · 2017
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Regularization, sparse recovery, and median-of-means tournaments
G. Lugosi and S. Mendelson · 2017
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RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets
L. Li, W. Xu, T. Chen, G. B. Giannakis, and Q. Ling · 2019
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Risk minimization by median-of-means tournaments
G. Lugosi and S. Mendelson · 2019
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Agnostic Federated Learning
M. Mohri, G. Sivek, and A. T. Suresh · 2019
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Federated Learning-Based Computation Offloading Optimization in Edge Computing-Supported Internet of Things
J. Ren, H. Wang, T. Hou, S. Zheng, and C. Tang · 2019
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Can You Really Backdoor Federated Learning?
Z. Sun, P. Kairouz, A. T. Suresh, and H. B. McMahan · 2019
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Robust Distributed Diffusion Recursive Least Squares Algorithms with Side Information for Adaptive Networks
Y. Yu, H. Zhao, R. C. de Lamare, Y. Zakharov, and L. Lu · 2019
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Federated Residual Learning
A. Agarwal, J. Langford, and C.-Y. Wei · 2020
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Hypothesis Testing Interpretations and Renyi Differential Privacy
B. Balle, G. Barthe, M. Gaboardi, J. Hsu, and T. Sato · 2020
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Secure Single-Server Aggregation with (Poly)Logarithmic Overhead
J. H. Bell, K. A. Bonawitz, A. Gascón, T. Lepoint, and M. Raykova · 2020
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Personalized Federated Learning with Moreau Envelopes
C. T. Dinh, N. H. Tran, and T. D. Nguyen · 2020
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Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
A. Fallah, A. Mokhtari, and A. E. Ozdaglar · 2020
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Scaffold: Stochastic Controlled Averaging for Federated Learning
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh · 2020
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Robust machine learning by median-of-means: Theory and practice
G. Lecué and M. Lerasle · 2020
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A Collaborative Learning Framework via Federated Meta-Learning
S. Lin, G. Yang, and J. Zhang · 2020
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An Invitation to Statistics in Wasserstein Space
V. M. Panaretos and Y. Zemel · 2020
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Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks
Z. Wu, Q. Ling, T. Chen, and G. B. Giannakis · 2020
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Federated Learning under Arbitrary Communication Patterns
D. Avdiukhin and S. P. Kasiviswanathan · 2021
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Federated Learning: A Signal Processing Perspective
T. Gafni, N. Shlezinger, K. Cohen, Y. C. Eldar, and H. V. Poor · 2021
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A Superquantile Approach to Federated Learning with Heterogeneous Devices
Y. Laguel, K. Pillutla, J. Malick, and Z. Harchaoui · 2021
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Adaptive Federated Optimization
S. J. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečný, S. Kumar, and H. B. McMahan · 2021
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A Field Guide to Federated Optimization
J. Wang, Z. Charles, Z. Xu, G. Joshi, H. B. McMahan, M. Al-Shedivat, G. Andrew, S. Avestimehr, K. Daly, D. Data, et al · 2021
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Collaborative Unsupervised Visual Representation Learning from Decentralized Data
W. Zhuang, X. Gan, Y. Wen, S. Zhang, and S. Yi · 2021
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