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Federated Learning (FL) is a setting where multiple parties with distributed data collaborate in training a joint Machine Learning (ML) model while keeping all data local at the parties.
A federated learning approach for mobile packet classification
Bakopoulou, E.; Tillman, B.; and Markopoulou, A. 2019 · 1907
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A survey on federated learning systems: vision, hype and reality for data privacy and protection
Li, Q.; Wen, Z.; Wu, Z.; Hu, S.; Wang, N.; and He, B. 2019 · 1907
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FCM: The fuzzy c-means clustering algorithm
Bezdek, J. C.; Ehrlich, R.; and Full, W. 1984 · 1984
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Reducing the time complexity of the fuzzy c-means algorithm
Kolen, J.; and Hutcheson, T. 2002 · 2002
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K-Means++: The Advantages of Careful Seeding
Arthur, D.; and Vassilvitskii, S. 2007 · 2007
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Scikit-learn: Machine Learning in Python
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; and Duchesnay, E. 2011 · 2011
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Fuzzy C- Means Algorithm- A Review
Suganya, R.; and Shanthi, R. 2012 · 2012
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Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data (…) (General Data Protection Regulation) OJ L 119, 4.5.2016, p. 1–88
EU. 2016 · 2016
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XNN graph
Mariescu-Istodor, P. F. R.; and Zhong, C. 2016 · 2016
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Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT
Deist, T. M.; Jochems, A.; van Soest, J.; Nalbantov, G.; Oberije, C.; Walsh, S.; Eble, M.; Bulens, P.; Coucke, P.; Dries, W.; et al. 2017 · 2017
Earlier work this paper cites.
The Elements of Statistical Learning – Data Mining, Inference and Prediction
Hastie, T.; Tibshirani, R.; and Friedman, J. 2017 · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Federated learning of predictive models from federated Electronic Health Records
Brisimi, T. S.; Chen, R.; Mela, T.; Olshevsky, A.; Paschalidis, I. C.; and Shi, W. 2018 · 2018
Cited alongside, same era.
Clustering basic benchmark (http://cs.uef.fi/sipu/datasets/)
Fränti, P.; and Sieranoja, S. 2018 · 2018
Cited alongside, same era.
Complex collaborative physical process management: a position on the trinity of BPM, IoT and DA
Grefen, P.; Ludwig, H.; Tata, S.; Dijkman, R.; Baracaldo, N.; Wilbik, A.; and D’hondt, T. 2018 · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction
Hard, A.; Rao, K.; Mathews, R.; Ramaswamy, S.; Beaufays, F.; Augenstein, S.; Eichner, H.; Kiddon, C.; and Ramage, D. 2018 · 2018
Cited alongside, same era.
Astraea: Self-Balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications
Duan, M.; Liu, D.; Chen, X.; Tan, Y.; Ren, J.; Qiao, L.; and Liang, L. 2019 · 2019
Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach
Ye, D.; Yu, R.; Pan, M.; and Han, Z. 2020 · 2020
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Cluster-Driven Graph Federated Learning Over Multiple Domains
Caldarola, D.; Mancini, M.; Galasso, F.; Ciccone, M.; Rodola, E.; and Caputo, B. 2021 · 2021
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Heterogeneity for the Win: One-Shot Federated Clustering
Dennis, D. K.; Li, T.; and Smith, V. 2021 · 2021
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Advances and Open Problems in Federated Learning
Kairouz, P.; and McMahan, H. B. 2021 · 2021
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Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
Khan, L. U.; Saad, W.; Han, Z.; Hossain, E.; and Hong, C. S. 2021 · 2021
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Dynamic Clustering in Federated Learning
Kim, Y.; Hakim, E. A.; Haraldson, J.; Eriksson, H.; da Silva, J. M. B.; and Fischione, C. 2021 · 2021
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Cited alongside, same era.
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
Wang, X.; Han, Y.; Wang, C.; Zhao, Q.; Chen, X.; and Chen, M. 2019 · 2019
Cited alongside, same era.
Multi-Task Network Anomaly Detection Using Federated Learning
Zhao, Y.; Chen, J.; Wu, D.; Teng, J.; and Yu, S. 2019 · 2019
Cited alongside, same era.
An Efficient Framework for Clustered Federated Learning
Ghosh, A.; Chung, J.; Yin, D.; and Ramchandran, K. 2020 · 2020
Cited alongside, same era.
Federated K-Means Clustering: A Novel Edge AI Based Approach for Privacy Preservation
Kumar, H. H.; V R, K.; and Nair, M. K. 2020 · 2020
Cited alongside, same era.
Practical federated gradient boosting decision trees
Li, Q.; Wen, Z.; and He, B. 2020 · 2020
Cited alongside, same era.
Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints
Sattler, F.; Muller, K.-R.; and Samek, W. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Federated FCM: Clustering Under Privacy Requirements
Pedrycz, W. 2021 · 2021
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A privacy-preserving and non-interactive federated learning scheme for regression training with gradient descent
Wang, F.; Zhu, H.; Lu, R.; Zheng, Y.; and Li, H. 2021 · 2021
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Multi-center federated learning
Xie, M.; Long, G.; Shen, T.; Zhou, T.; Wang, X.; Jiang, J.; and Zhang, C. 2021 · 2021
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A Comprehensive Survey of Privacy-Preserving Federated Learning: A Taxonomy, Review, and Future Directions
Yin, X.; Zhu, Y.; and Hu, J. 2021 · 2021
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Federated Learning on Non-IID Data: A Survey
Zhu, H.; Xu, J.; Liu, S.; and Jin, Y. 2021 · 2021
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