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Federated Learning (FL) is a distributed, and decentralized machine learning protocol.
Proofs of Work and Bread Pudding Protocols(Extended Abstract)
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Practical Byzantine Fault Tolerance and Proactive Recovery
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BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning
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CIFAR-10 (Canadian Institute for Advanced Research)
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Satoshi Nakamoto. 2009 · 2009
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Differentially private federated learning: A client level perspective
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Federated Learning: Strategies for Improving Communication Efficiency
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Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains. In Proceedings of the Thirteenth EuroSys Conference (Porto, Portugal) (EuroSys ’18) . Association for Computing Machinery, New York, NY, USA, Article 30, 15 pages
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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. In International Conference on Machine Learning . 5650–5659
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. 2018 · 2018
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Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning . 634–643
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. 2019 · 2019
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Fastfabric: Scaling hyperledger fabric to 20,000 transactions per second. In 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) . IEEE, 455–463
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Robust aggregation for federated learning
Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui. 2019 · 2019
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Robust and communication-efficient federated learning from non-iid data
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Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Anima Anandkumar. 2018 · 2018
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Adjudicating Violations in Data Sharing Agreements Using Smart Contracts. In 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) . 1553–1560
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Performance Analysis of Consensus Algorithm in Private Blockchain. In 2018 IEEE Intelligent Vehicles Symposium (IV) . 280–285
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Measuring ethereum network peers. In Proceedings of the Internet Measurement Conference 2018 . 91–104
Seoung Kyun Kim, Zane Ma, Siddharth Murali, Joshua Mason, Andrew Miller, and Michael Bailey. 2018 · 2018
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Trojaning Attack on Neural Networks. In 25nd Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, February 18-221, 2018 . The Internet Society
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The hidden vulnerability of distributed learning in byzantium
El Mahdi El Mhamdi, Rachid Guerraoui, and Sébastien Rouault. 2018 · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks. In Advances in Neural Information Processing Systems . 6103–6113
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein. 2018 · 2018
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Common architectures in convolutional neural networks
[n.d.]
Cited in the paper.
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2016a
Cited in the paper.
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek. 2019 · 2019
Later among the works it cites.
Overcoming Forgetting in Federated Learning on Non-IID Data
Neta Shoham, Tomer Avidor, Aviv Keren, Nadav Israel, Daniel Benditkis, Liron Mor-Yosef, and Itai Zeitak. 2019 · 2019
Later among the works it cites.
Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan. 2019 · 2019
Later among the works it cites.
How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics . 2938–2948
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020 · 2020
Closest in time.
Mitigating Sybils in Federated Learning Poisoning
Clement Fung, Chris J. M. Yoon, and Ivan Beschastnikh. 2020 · 2020
Closest in time.
Defending Against Backdoors in Federated Learning with Robust Learning Rate
Mustafa Safa Ozdayi, Murat Kantarcioglu, and Yulia R Gel. 2020 · 2020
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