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Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016 · 2016
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017 · 2017
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Gaia: Geo-distributed machine learning approaching { \{ LAN } \} speeds. In 14th { \{ USENIX } \} Symposium on Networked Systems Design and Implementation ( { \{ NSDI } \} 17) . 629–647
Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R Ganger, Phillip B Gibbons, and Onur Mutlu. 2017 · 2017
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Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics . PMLR, 1273–1282
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017 · 2017
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Federated learning: Collaborative machine learning without centralized training data
Brendan McMahan and Daniel Ramage. 2017 · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Asynchronous federated learning for geospatial applications. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 21–28
Michael R Sprague, Amir Jalalirad, Marco Scavuzzo, Catalin Capota, Moritz Neun, Lyman Do, and Michael Kopp. 2018 · 2018
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Flchain: A blockchain for auditable federated learning with trust and incentive. In 2019 5th International Conference on Big Data Computing and Communications (BIGCOM) . IEEE, 151–159
Xianglin Bao, Cheng Su, Yan Xiong, Wenchao Huang, and Yifei Hu. 2019 · 2019
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
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Neel Guha, Ameet Talwalkar, and Virginia Smith. 2019 · 2019
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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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Incentive design for efficient federated learning in mobile networks: A contract theory approach. In 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS) . IEEE, 1–5
Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, and Dong In Kim. 2019 · 2019
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Cmfl: Mitigating communication overhead for federated learning. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) . IEEE, 954–964
WANG Luping, WANG Wei, and LI Bo. 2019 · 2019
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Efficient and private federated learning using tee. In EuroSys
Fan Mo and Hamed Haddadi. 2019 · 2019
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BAFFLE: Blockchain based aggregator free federated learning
Paritosh Ramanan, Kiyoshi Nakayama, and Ratnesh Sharma. 2019 · 2019
Cited alongside, same era.
Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan. 2019 · 2019
Cited alongside, same era.
Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta. 2019 · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019 · 2019
Cited alongside, same era.
AI pandemic engine
2020 · 2020
Cited alongside, same era.
Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging
Rajesh Kumar, Abdullah Aman Khan, Sinmin Zhang, WenYong Wang, Yousif Abuidris, Waqas Amin, and Jay Kumar. 2020 · 2020
Later among the works it cites.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020 · 2020
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Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
Yi Liu, JQ James, Jiawen Kang, Dusit Niyato, and Shuyu Zhang. 2020 · 2020
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A survey on security and privacy of federated learning
Viraaji Mothukuri, Reza M Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava. 2020 · 2020
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Data Poisoning Attacks on Federated Machine Learning
Gan Sun, Yang Cong, Jiahua Dong, Qiang Wang, and Ji Liu. 2020 · 2020
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Federated learning: A survey on enabling technologies, protocols, and applications
Mohammed Aledhari, Rehma Razzak, Reza M Parizi, and Fahad Saeed. 2020 · 2020
Cited alongside, same era.
How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics . PMLR, 2938–2948
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020 · 2020
Cited alongside, same era.
Federated learning for privacy-preserving AI
Yong Cheng, Yang Liu, Tianjian Chen, and Qiang Yang. 2020 · 2020
Cited alongside, same era.
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture
Harsh Bimal Desai, Mustafa Safa Ozdayi, and Murat Kantarcioglu. 2020 · 2020
Cited alongside, same era.
Local model poisoning attacks to Byzantine-robust federated learning. In 29th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 20) . 1605–1622
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong. 2020 · 2020
Cited alongside, same era.
Decentralizing Large-Scale Natural Language Processing with Federated Learning
Daniel Garcia Bernal. 2020 · 2020
Cited alongside, same era.
A Review of Challenges and Opportunities in Machine Learning for Health
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, Irene Y Chen, and Rajesh Ranganath. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Federated Machine Learning in Vehicular Networks: A summary of Recent Applications. In 2020 International Conference on UK-China Emerging Technologies (UCET) . IEEE, 1–4
Kang Tan, Duncan Bremner, Julien Le Kernec, and Muhammad Imran. 2020 · 2020
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Data Poisoning Attacks Against Federated Learning Systems. In European Symposium on Research in Computer Security . Springer, 480–501
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy, and Ling Liu. 2020 · 2020
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Marten van Dijk, Nhuong V Nguyen, Toan N Nguyen, Lam M Nguyen, Quoc Tran-Dinh, and Phuong Ha Nguyen. 2020 · 2020
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A Sustainable Incentive Scheme for Federated Learning
Han Yu, Zelei Liu, Yang Liu, Tianjian Chen, Mingshu Cong, Xi Weng, Dusit Niyato, and Qiang Yang. 2020b · 2020
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Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning. In 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI) . IEEE, 104–115
Tianlong Yu, Tian Li, Yuqiong Sun, Susanta Nanda, Virginia Smith, Vyas Sekar, and Srinivasan Seshan. 2020a · 2020
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A learning-based incentive mechanism for federated learning
Yufeng Zhan, Peng Li, Zhihao Qu, Deze Zeng, and Song Guo. 2020 · 2020
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Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning. In 2020 { \{ USENIX } \} Annual Technical Conference ( { \{ USENIX } \} { \{ ATC } \} 20) . 493–506
Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu. 2020 · 2020
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Distilled One-Shot Federated Learning
Yanlin Zhou, George Pu, Xiyao Ma, Xiaolin Li, and Dapeng Wu. 2020 · 2020
Later among the works it cites.