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Federated learning can be a promising solution for enabling IoT cybersecurity (i.e., anomaly detection in the IoT environment) while preserving data privacy and mitigating the high communication/storage overhead (e.g., high-frequency data from time-series sensors) of centralized over-the-cloud approaches.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
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Mqtt-s—a publish/subscribe protocol for wireless sensor networks
Urs Hunkeler, Hong Linh Truong, and Andy Stanford-Clark · 2008
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Federated learning of deep networks using model averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
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Botnets and internet of things security
E. Bertino and N. Islam · 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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Deep gradient compression: Reducing the communication bandwidth for distributed training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J Dally · 2017
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N-baiot—network-based detection of iot botnet attacks using deep autoencoders
Yair Meidan, Michael Bohadana, Yael Mathov, Yisroel Mirsky, Asaf Shabtai, Dominik Breitenbacher, and Yuval Elovici · 2018
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Communication compression for decentralized training
Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, and Ji Liu · 2018
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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DÏot: A federated self-learning anomaly detection system for iot
Thien Nguyen, Samuel Marchal, Markus Miettinen, Hossein Fereidooni, N. Asokan, and Ahmad-Reza Sadeghi · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Iotdefender: A federated transfer learning intrusion detection framework for 5g iot
Yulin Fan, Yang Li, Mengqi Zhan, Huajun Cui, and Yan Zhang · 2020
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Deep anomaly detection for time-series data in industrial iot: A communication-efficient on-device federated learning approach
Yi Liu, Sahil Garg, Jiangtian Nie, Yang Zhang, Zehui Xiong, Jiawen Kang, and M. Shamim Hossain · 2020
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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An on-device federated learning approach for cooperative anomaly detection, 02 2020
Rei Ito, Mineto Tsukada, and Hiroki Matsutani · 2020
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Federated learning for malware detection in iot devices
Valerian Rey, Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Gérôme Bovet, and Martin Jaggi · 2021
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Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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A dynamic and hierarchical access control for iot in multi-authority cloud storage
Khaled Riad, Teng Huang, and Lishan Ke · 2020
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Local adaptivity in federated learning: Convergence and consistency
Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary B. Charles, Luyang Liu, and Gauri Joshi · 2021
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