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Federated learning (FL) allows participants to jointly train a machine learning model without sharing their private data with others.
Nonparametric bayesian estimators for counting processes
Yongdai Kim · 1999
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Hierarchical beta processes and the indian buffet process
Romain Thibaux and Michael I Jordan · 2007
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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Distance dependent chinese restaurant processes
David M Blei and Peter I Frazier · 2010
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A stick-breaking construction of the beta process
John W Paisley, Aimee K Zaas, Christopher W Woods, Geoffrey S Ginsburg, and Lawrence Carin · 2010
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Spectral chinese restaurant processes: Nonparametric clustering based on similarities
Richard Socher, Andrew Maas, and Christopher Manning · 2011
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Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
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Auror: Defending Against Poisoning Attacks in Collaborative Deep Learning Systems
Shiqi Shen, Shruti Tople, and Prateek Saxena · 2016
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Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Lectures on the Poisson process
Günter Last and Mathew Penrose · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Federated learning: Collaborative Machine Learning without Centralized Training Data
Brendan McMahan and Daniel Ramage · 2017
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Learning differentially private recurrent language models
Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
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Posteriors, conjugacy, and exponential families for completely random measures
Tamara Broderick, Ashia C Wilson, and Michael I Jordan · 2018
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The hidden vulnerability of distributed learning in byzantium
Rachid Guerraoui, Sébastien Rouault, et al · 2018
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Learning Differentially Private Language Models Without Losing Accuracy
Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
Micah Sheller, Anthony Reina, Brandon Edwards, Jason Martin, and Spyridon Bakas · 2018
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Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics
Arunan Sivanathan, Hassan Habibi Gharakheili, Franco Loi, Adam Radford, Chamith Wijenayake, Arun Vishwanath, and Vijay Sivaraman · 2018
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Applied federated learning: Improving google keyboard query suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
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Sever: A robust meta-algorithm for stochastic optimization
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart · 2019
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Abnormal client behavior detection in federated learning
Suyi Li, Yong Cheng, Yang Liu, Wei Wang, and Tianjian Chen · 2019
Poisoning Attacks on Federated Learning-Based IoT Intrusion Detection System
Thien Duc Nguyen, Phillip Rieger, Markus Miettinen, and Ahmad-Reza Sadeghi · 2020
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Attack of the tails: Yes, you really can backdoor federated learning
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos · 2020
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Mitigating backdoor attacks in federated learning
Chen Wu, Xian Yang, Sencun Zhu, and Prasenjit Mitra · 2020
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DBA: Distributed Backdoor Attacks against Federated Learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li · 2020
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BaFFLe: Backdoor Detection via Feedback-based Federated Learning
Sebastien Andreina, Giorgia Azzurra Marson, Helen Möllering, and Ghassan Karame · 2021
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Cited alongside, same era.
A tutorial on dirichlet process mixture modeling
Yuelin Li, Elizabeth Schofield, and Mithat Gönen · 2019
Cited alongside, same era.
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging
Luis Muñoz-González, Kenneth T. Co, and Emil C. Lupu · 2019
Cited alongside, same era.
DÏoT: A Federated Self-learning Anomaly Detection System for IoT
Thien Duc Nguyen, Samuel Marchal, Markus Miettinen, Hossein Fereidooni, N. Asokan, and Ahmad-Reza Sadeghi · 2019
Cited alongside, same era.
Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and Brendan McMahan · 2019
Cited alongside, same era.
Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, 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.
Local model poisoning attacks to byzantine-robust federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong · 2020
Cited alongside, same era.
Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong · 2021
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Provably secure federated learning against malicious clients
Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2021
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Safelearn: Secure aggregation for private federated learning
Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Helen Möllering, Thien Duc Nguyen, Phillip Rieger, Ahmad-Reza Sadeghi, Thomas Schneider, Hossein Yalame, et al · 2021
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Crfl: Certifiably robust federated learning against backdoor attacks
Chulin Xie, Minghao Chen, Pin-Yu Chen, and Bo Li · 2021
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Federated learning for healthcare informatics
Jie Xu, Benjamin S Glicksberg, Chang Su, Peter Walker, Jiang Bian, and Fei Wang · 2021
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FedCRI: Federated Mobile Cyber-Risk Intelligence
Hossein Fereidooni, Alexandra Dmitrienko, Phillip Rieger, Markus Miettinen, Ahmad-Reza Sadeghi, and Felix Madlener · 2022
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Local and central differential privacy for robustness and privacy in federated learning
Mohammad Naseri, Jamie Hayes, and Emiliano De Cristofaro · 2022
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FLAME: taming backdoors in federated learning
Thien Duc Nguyen, Phillip Rieger, Huili Chen, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Farinaz Koushanfar, Ahmad-Reza Sadeghi, Thomas Schneider, and Shaza Zeitouni · 2022
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Robust aggregation for federated learning
Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui · 2022
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Phillip Rieger, Torsten Krauß, Markus Miettinen, Alexandra Dmitrienko, and Ahmad-Reza Sadeghi · 2022
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Deepsight: Mitigating backdoor attacks in federated learning through deep model inspection
Phillip Rieger, Thien Duc Nguyen, Markus Miettinen, and Ahmad-Reza Sadeghi · 2022
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