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Federated Learning (FL) is a promising approach enabling multiple clients to train Deep Neural Networks (DNNs) collaboratively without sharing their local training data.
The three sigma rule
Friedrich Pukelsheim · 1994
Earlier work this paper cites.
https://www.govinfo.gov/content/pkg/PLAW-104publ191/pdf/PLAW-104publ191.pdf
Health Insurance Portability and Accountability Act, 1996 · 1996
Earlier work this paper cites.
A density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al · 1996
Earlier work this paper cites.
A comparison of tests of equality of variances
Tjen-Sien Lim and Wei-Yin Loh · 1996
Earlier work this paper cites.
Estimating a dirichlet distribution
Thomas Minka · 2003
Earlier work this paper cites.
Who was student and why do we care so much about his t-test? 1
Edward H Livingston · 2004
Earlier work this paper cites.
Mathematical statistics and data analysis
John A Rice · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Antidote: understanding and defending against poisoning of anomaly detectors
Benjamin IP Rubinstein, Blaine Nelson, Ling Huang, Anthony D Joseph, Shing-hon Lau, Satish Rao, Nina Taft, and J Doug Tygar · 2009
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
Earlier work this paper cites.
Multiple-gradient descent algorithm (mgda) for multiobjective optimization
Jean-Antoine Désidéri · 2012
Earlier work this paper cites.
https://eur-lex.europa.eu/eli/reg/2016/679/oj
General Data Protection Regulation, 2018 · 2016
Earlier work this paper cites.
Intel sgx explained
Victor Costan and Srinivas Devadas · 2016
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Amd memory encryption
David Kaplan, Jeremy Powell, and Tom Woller · 2016
Earlier work this paper cites.
Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Earlier work this paper cites.
Hierarchical Clustering
Frank Nielsen · 2016
Earlier work this paper cites.
Auror: Defending Against Poisoning Attacks in Collaborative Deep Learning Systems
Shiqi Shen, Shruti Tople, and Prateek Saxena · 2016
Earlier work this paper cites.
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
Earlier work this paper cites.
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Earlier work this paper cites.
Software grand exposure: SGX cache attacks are practical
Ferdinand Brasser, Urs Müller, Alexandra Dmitrienko, Kari Kostiainen, Srdjan Capkun, and Ahmad-Reza Sadeghi · 2017
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
Earlier work this paper cites.
Federated learning: Collaborative Machine Learning without Centralized Training Data
Brendan McMahan and Daniel Ramage · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=201720180SB1121
California Consumer Privacy Act, 2018 · 2018
Earlier work this paper cites.
Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
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Federated meta-learning with fast convergence and efficient communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
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Property inference attacks on fully connected neural networks using permutation invariant representations
Karan Ganju, Qi Wang, Wei Yang, Carl A Gunter, and Nikita Borisov · 2018
Earlier work this paper cites.
The hidden vulnerability of distributed learning in byzantium
Rachid Guerraoui, Sébastien Rouault, et al · 2018
Earlier work this paper cites.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
Earlier work this paper cites.
Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and X. Zhang · 2018
Earlier work this paper cites.
Learning Differentially Private Language Models Without Losing Accuracy
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Knock knock, who’s there? membership inference on aggregate location data
Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro · 2018
Cited alongside, same era.
Federated Learning for Medical Imaging
Micah Sheller, Anthony Reina, Brandon Edwards, Jason Martin, and Spyridon Bakas · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Graviton: Trusted execution environments on gpus
Stavros Volos, Kapil Vaswani, and Rodrigo Bruno · 2018
Cited alongside, same era.
Updates-leak: Data set inference and reconstruction attacks in online learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, and Yang Zhang · 2020
Later among the works it cites.
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Micah J. Sheller, Brandon Edwards, G. Anthony Reina, Jason Martin, Sarthak Pati, Aikaterini Kotrotsou, Mikhail Milchenko, Weilin Xu, Daniel Marcus, Rivka R. Colen, and Spyridon Bakas · 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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Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks
Zhaoxian Wu, Qing Ling, Tianyi Chen, and Georgios B. Giannakis · 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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Jinwen Wang, Yueqiang Cheng, Qi Li, and Yong Jiang · 2018
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
Cited alongside, same era.
A new backdoor attack in cnns by training set corruption without label poisoning
Mauro Barni, Kassem Kallas, and Benedetta Tondi · 2019
Cited alongside, same era.
Dr. sgx: Automated and adjustable side-channel protection for sgx using data location randomization
Ferdinand Brasser, Srdjan Capkun, Alexandra Dmitrienko, Tommaso Frassetto, Kari Kostiainen, and Ahmad-Reza Sadeghi · 2019
Cited alongside, same era.
Logan: Membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2019
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
Cited alongside, same era.
Rsa: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets
Liping Li, Wei Xu, Tianyi Chen, Georgios B Giannakis, and Qing Ling · 2019
Cited alongside, same era.
idlg: Improved deep leakage from gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
Later among the works it cites.
Shielding collaborative learning: Mitigating poisoning attacks through client-side detection
Lingchen Zhao, Shengshan Hu, Qian Wang, Jianlin Jiang, Chao Shen, Xiangyang Luo, and Pengfei Hu · 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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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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Feddropoutavg: Generalizable federated learning for histopathology image classification
Gozde N Gunesli, Mohsin Bilal, Shan E Ahmed Raza, and Nasir M Rajpoot · 2021
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Byzantine-robust and privacy-preserving framework for fedml
Hanieh Hashemi, Yongqin Wang, Chuan Guo, and Murali Annavaram · 2021
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Aion attacks: Manipulating software timers in trusted execution environment
Wei Huang, Shengjie Xu, Yueqiang Cheng, and David Lie · 2021
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Fedbn: Federated learning on non-iid features via local batch normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou · 2021
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Ppfl: privacy-preserving federated learning with trusted execution environments
Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis · 2021
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Cacheout: Leaking data on intel cpus via cache evictions
Stephan van Schaik, Marina Minkin, Andrew Kwong, Daniel Genkin, and Yuval Yarom · 2021
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Federated learning on non-iid data: A survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu, and Yaochu Jin · 2021
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https://pytorch.org
Pytorch, 2022 · 2022
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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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Openfl: the open federated learning library
Patrick Foley, Micah J Sheller, Brandon Edwards, Sarthak Pati, Walter Riviera, Mansi Sharma, Prakash Narayana Moorthy, Shi-han Wang, Jason Martin, Parsa Mirhaji, Prashant Shah, and Spyridon Bakas · 2022
Closest in time.
Memory optimization system for sgxv2 trusted execution environment
Mingyu Li, Yubin Xia, and Haibo Chen · 2022
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Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives
Pengrui Liu, Xiangrui Xu, and Wei Wang · 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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Nvidia h100 tensor core gpu architecture
Nvidia · 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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Pridwen: Universally hardening sgx programs via load-time synthesis
Fan Sang, Ming-Wei Shih, Sangho Lee, Xiaokuan Zhang, Michael Steiner, Mona Vij, and Taesoo Kim · 2022
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Flare: defending federated learning against model poisoning attacks via latent space representations
Ning Wang, Yang Xiao, Yimin Chen, Yang Hu, Wenjing Lou, and Y Thomas Hou · 2022
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https://www.med.upenn.edu/cbica/fets/#FeTSCollaboratingSites6
The federated tumor segmentation (FeTS) initiative · 2023
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Baybfed: Bayesian backdoor defense for federated learning
Kavita Kumari, Phillip Rieger, Hossein Fereidooni, Murtuza Jadliwala, and Ahmad-Reza Sadeghi · 2023
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Flairs: Fpga-accelerated inference-resistant & secure federated learning
Huimin Li, Phillip Rieger, Shaza Zeitouni, Stjepan Picek, and Ahmad-Reza Sadeghi · 2023
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