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Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive local datasets with others.
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.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
A Fully Homomorphic Encryption Scheme
Craig Gentry · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Density-Based Clustering Based on Hierarchical Density Estimates
Ricardo J. G. B. Campello, Davoud Moulavi, and Joerg Sander · 2013
Earlier work this paper cites.
Project Adam: Building an Efficient and Scalable Deep Learning Training System
Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman · 2014
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The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth · 2014
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ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation
Daniel Demmler, Thomas Schneider, and Michael Zohner · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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.
https://bigquery.cloud.google.com/dataset/fh-bigquery:reddit_comments
Reddit dataset, 2017 · 2017
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Understanding the Mirai Botnet
Manos Antonakakis, Tim April, Michael Bailey, Matt Bernhard, Elie Bursztein, Jaime Cochran, Zakir Durumeric, J. Alex Halderman, Luca Invernizzi, Michalis Kallitsis, Deepak Kumar, Chaz Lever, Zane Ma, Joshua Mason, Damian Menscher, Chad Seaman, Nick Sullivan, Kurt Thomas, and Yi Zhou · 2017
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Privacy-preserving Deep Learning via Additively Homomorphic Encryption
Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai · 2017
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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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DDoS in the IoT: Mirai and Other Botnets
Constantinos Kolias, Georgios Kambourakis, Angelos Stavrou, and Jeffrey Voas · 2017
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Accelerated hierarchical density based clustering
Leland McInnes and John Healy · 2017
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hdbscan: Hierarchical density based clustering
Leland McInnes, John Healy, and Steve Astels · 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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Membership Inference Attacks Against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Federated Multi-Task Learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning
Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu · 2017
Earlier work this paper cites.
Machine Learning DDoS Detection for Consumer Internet of Things Devices
Rohan Doshi, Noah Apthorpe, and Nick Feamster · 2018
Cited alongside, same era.
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
Cited alongside, same era.
The hidden vulnerability of distributed learning in byzantium
Rachid Guerraoui, Sébastien Rouault, et al · 2018
Cited alongside, same era.
LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data
Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, and Dianbo Liu · 2018
Cited alongside, same era.
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J. Dally · 2018
Cited alongside, same era.
Learning Differentially Private Language Models Without Losing Accuracy
Federated Learning-Based Computation Offloading Optimization in Edge Computing-Supported Internet of Things
Jianji Ren, Haichao Wang, Tingting Hou, Shuai Zheng, and Chaosheng Tang · 2019
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Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
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Eavesdrop the Composition Proportion of Training Labels in Federated Learning
Lixu Wang, Shichao Xu, Xiao Wang, and Qi Zhu · 2019
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How To Backdoor Federated Learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
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Robust anomaly detection and backdoor attack detection via differential privacy
Min Du, Ruoxi Jia, and Dawn Song · 2020
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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 Ultra-Reliable Low-Latency V2V Communications
Sumudu Samarakoon, Mehdi Bennis, Walid Saad, and Merouane Debbah · 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.
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
Cited alongside, same era.
BlackIoT: IoT Botnet of High Wattage Devices Can Disrupt the Power Grid
Saleh Soltan, Prateek Mittal, and Vincent Poor · 2018
Cited alongside, same era.
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2020
Later among the works it cites.
The limitations of federated learning in sybil settings
Clement Fung, Chris JM Yoon, and Ivan Beschastnikh · 2020
Later among the works it cites.
Mlguard: Mitigating poisoning attacks in privacy preserving distributed collaborative learning
Youssef Khazbak, Tianxiang Tan, and Guohong Cao · 2020
Later among the works it cites.
Learning to detect malicious clients for robust federated learning
Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen · 2020
Later among the works it cites.
Poisoning Attacks on Federated Learning-Based IoT Intrusion Detection System
Thien Duc Nguyen, Phillip Rieger, Markus Miettinen, and Ahmad-Reza Sadeghi · 2020
Later among the works it cites.
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
Later among the works it cites.
DBA: Distributed Backdoor Attacks against Federated Learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li · 2020
Later among the works it cites.
BaFFLe: Backdoor Detection via Feedback-based Federated Learning
Sebastien Andreina, Giorgia Azzurra Marson, Helen Möllering, and Ghassan Karame · 2021
Closest in time.
Privacy-preserving density-based clustering
Beyza Bozdemir, Sébastien Canard, Orhan Ermis, Helen Möllering, Melek Önen, and Thomas Schneider · 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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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
Closest in time.
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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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, Shaza Zeitouni, Farinaz Koushanfar, Ahmad-Reza Sadeghi, and Thomas Schneider · 2022
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More is better (mostly): On the backdoor attacks in federated graph neural networks
Jing Xu, Rui Wang, Stefanos Koffas, Kaitai Liang, and Stjepan Picek · 2022
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More is better (mostly): On the backdoor attacks in federated graph neural networks, 2022
Jing Xu, Rui Wang, Stefanos Koffas, Kaitai Liang, and Stjepan Picek · 2022
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https://github.com/haoyangliASTAPLE/3DFed , 2023
3dfed source code · 2023
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3dfed: Adaptive and extensible framework for covert backdoor attack in federated learning
Haoyang Li, Qingqing Ye, Haibo Hu, Jin Li, Leixia Wang, Chengfang Fang, and Jie Shi · 2023
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