Federated learning (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Federated learning" in English language version.

refsWebsite

Global rank English rank

25arxiv.org

69^th place

59^th place

17doi.org

2^nd place

10semanticscholar.org

11^th place

8^th place

8nih.gov

4^th place

3worldcat.org

5^th place

2ieee.org

652^nd place

515^th place

1nowpublishers.com

low place

1mlr.press

low place

1apache.org

3,206^th place

2,477^th place

1neuroquantology.com

low place

1academia.edu

121^st place

142^nd place

1handle.net

102^nd place

76^th place

1nature.com

234^th place

397^th place

1neurips.cc

low place

1harvard.edu

18^th place

17^th place

1mlcommons.org

low place

academia.edu

Berhanu, Yoseph. "A Framework for Multi-source Prefetching Through Adaptive Weight".

apache.org

wayang.apache.org

"Apache Wayang - Home". wayang.apache.org.

arxiv.org

Kairouz, Peter; McMahan, H. Brendan; Avent, Brendan; Bellet, Aurélien; Bennis, Mehdi; Bhagoji, Arjun Nitin; Bonawitz, Kallista; Charles, Zachary; Cormode, Graham; Cummings, Rachel; D’Oliveira, Rafael G. L.; Eichner, Hubert; Rouayheb, Salim El; Evans, David; Gardner, Josh (2021-06-22). "Advances and Open Problems in Federated Learning". Foundations and Trends in Machine Learning. 14 (1–2): 1–210. arXiv:1912.04977. doi:10.1561/2200000083. ISSN 1935-8237.
Konečný, Jakub; McMahan, Brendan; Ramage, Daniel (2015). "Federated Optimization: Distributed Optimization Beyond the Datacenter". arXiv:1511.03575 [cs.LG].
Kairouz, Peter; Brendan McMahan, H.; Avent, Brendan; Bellet, Aurélien; Bennis, Mehdi; Arjun Nitin Bhagoji; Bonawitz, Keith; Charles, Zachary; Cormode, Graham; Cummings, Rachel; D'Oliveira, Rafael G. L.; Salim El Rouayheb; Evans, David; Gardner, Josh; Garrett, Zachary; Gascón, Adrià; Ghazi, Badih; Gibbons, Phillip B.; Gruteser, Marco; Harchaoui, Zaid; He, Chaoyang; He, Lie; Huo, Zhouyuan; Hutchinson, Ben; Hsu, Justin; Jaggi, Martin; Javidi, Tara; Joshi, Gauri; Khodak, Mikhail; et al. (10 December 2019). "Advances and Open Problems in Federated Learning". arXiv:1912.04977 [cs.LG].
Xu, Zirui; Yu, Fuxun; Xiong, Jinjun; Chen, Xiang (December 2021). "Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration". 2021 58th ACM/IEEE Design Automation Conference (DAC). pp. 997–1002. arXiv:1912.01684. doi:10.1109/DAC18074.2021.9586241. ISBN 978-1-6654-3274-0. S2CID 243925551.
Diao, Enmao; Ding, Jie; Tarokh, Vahid (2020-10-02). "HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients". arXiv:2010.01264 [cs.LG].
Yu, Fuxun; Zhang, Weishan; Qin, Zhuwei; Xu, Zirui; Wang, Di; Liu, Chenchen; Tian, Zhi; Chen, Xiang (2021-08-14). "Fed2". Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. KDD '21. New York, NY, USA: Association for Computing Machinery. pp. 2066–2074. arXiv:2111.14248. doi:10.1145/3447548.3467309. ISBN 978-1-4503-8332-5. S2CID 240598436.
Savazzi, Stefano; Nicoli, Monica; Rampa, Vittorio (May 2020). "Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks". IEEE Internet of Things Journal. 7 (5): 4641–4654. arXiv:1912.13163. doi:10.1109/JIOT.2020.2964162. S2CID 209515403.
Gupta, Otkrist; Raskar, Ramesh (14 October 2018). "Distributed learning of deep neural network over multiple agents". arXiv:1810.06060 [cs.LG].
Vepakomma, Praneeth; Gupta, Otkrist; Swedish, Tristan; Raskar, Ramesh (3 December 2018). "Split learning for health: Distributed deep learning without sharing raw patient data". arXiv:1812.00564 [cs.LG].
Hsieh, Kevin; Phanishayee, Amar; Mutlu, Onur; Gibbons, Phillip (2020-11-21). "The Non-IID Data Quagmire of Decentralized Machine Learning". International Conference on Machine Learning. PMLR: 4387–4398. arXiv:1910.00189.
Bagdasaryan, Eugene; Veit, Andreas; Hua, Yiqing (2019-08-06). "How To Backdoor Federated Learning". arXiv:1807.00459 [cs.CR].
Reddi, Sashank; Charles, Zachary; Zaheer, Manzil; Garrett, Zachary; Rush, Keith; Konečný, Jakub; Kumar, Sanjiv; McMahan, H. Brendan (2021-09-08), Adaptive Federated Optimization, doi:10.48550/arXiv.2003.00295, retrieved 2024-07-24
Acar, Durmus Alp Emre; Zhao, Yue; Navarro, Ramon Matas; Mattina, Matthew; Whatmough, Paul N.; Saligrama, Venkatesh (2021). "Federated Learning Based on Dynamic Regularization". ICLR. arXiv:2111.04263.
Vahidian, Saeed; Morafah, Mahdi; Lin, Bill (2021). "Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity". Icdcs-W. arXiv:2105.00562.
Yeganeh, Yousef; Farshad, Azade; Navab, Nassir; Albarqouni, Shadi (2020). "Inverse Distance Aggregation for Federated Learning with Non-IID Data". Icdcs-W. arXiv:2008.07665.
Overman, T., Blum, G., & Klabjan, D.. (2024). A Primal-Dual Algorithm for Hybrid Federated Learning, https://arxiv.org/pdf/2210.08106.pdf
Konečný, Jakub; McMahan, H. Brendan; Yu, Felix X.; Richtárik, Peter; Suresh, Ananda Theertha; Bacon, Dave (30 October 2017). "Federated Learning: Strategies for Improving Communication Efficiency". arXiv:1610.05492 [cs.LG].
Amiri, Mohammad Mohammadi; Gunduz, Deniz (10 February 2020). "Federated Learning over Wireless Fading Channels". arXiv:1907.09769 [cs.IT].
Xian, Xun; Wang, Xinran; Ding, Jie; Ghanadan, Reza (2020). "Assisted Learning: A Framework for Multi-Organization Learning". Advances in Neural Information Processing Systems. 33. arXiv:2004.00566.
Elbir, Ahmet M.; Coleri, S. (2 June 2020). "Federated Learning for Vehicular Networks". arXiv:2006.01412 [eess.SP].
Rieke, Nicola; Hancox, Jonny; Li, Wenqi; Milletarì, Fausto; Roth, Holger R.; Albarqouni, Shadi; Bakas, Spyridon; Galtier, Mathieu N.; Landman, Bennett A.; Maier-Hein, Klaus; Ourselin, Sébastien; Sheller, Micah; Summers, Ronald M.; Trask, Andrew; Xu, Daguang; Baust, Maximilian; Cardoso, M. Jorge (14 September 2020). "The future of digital health with federated learning". npj Digital Medicine. 3 (1): 119. arXiv:2003.08119. doi:10.1038/s41746-020-00323-1. PMC 7490367. PMID 33015372. S2CID 212747909.
Karargyris, Alexandros; Umeton, Renato; Sheller, Micah J.; et al. (17 July 2023). "Federated benchmarking of medical artificial intelligence with MedPerf". Nature Machine Intelligence. 5 (7). Springer Science and Business Media LLC: 799–810. arXiv:2110.01406. doi:10.1038/s42256-023-00652-2. ISSN 2522-5839. PMC 11068064. PMID 38706981.
Liu, Boyi; Wang, Lujia; Liu, Ming (2019). "Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems". 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 1688–1695. arXiv:1901.06455. doi:10.1109/IROS40897.2019.8967908. ISBN 978-1-7281-4004-9. S2CID 210972473.
Na, Seongin; Rouček, Tomáš; Ulrich, Jiří; Pikman, Jan; Krajník, Tomáš; Lennox, Barry; Arvin, Farshad (2023). "Federated Reinforcement Learning for Collective Navigation of Robotic Swarms". IEEE Transactions on Cognitive and Developmental Systems. 15 (4): 1. arXiv:2202.01141. doi:10.1109/TCDS.2023.3239815. S2CID 246473085.
Yu, Xianjia; Queralta, Jorge Pena; Westerlund, Tomi (2022). "Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer". Procedia Computer Science. 210: 86–93. arXiv:2205.15496. doi:10.1016/j.procs.2022.10.123.

doi.org

Kairouz, Peter; McMahan, H. Brendan; Avent, Brendan; Bellet, Aurélien; Bennis, Mehdi; Bhagoji, Arjun Nitin; Bonawitz, Kallista; Charles, Zachary; Cormode, Graham; Cummings, Rachel; D’Oliveira, Rafael G. L.; Eichner, Hubert; Rouayheb, Salim El; Evans, David; Gardner, Josh (2021-06-22). "Advances and Open Problems in Federated Learning". Foundations and Trends in Machine Learning. 14 (1–2): 1–210. arXiv:1912.04977. doi:10.1561/2200000083. ISSN 1935-8237.
Pokhrel, Shiva Raj; Choi, Jinho (2020). "Federated Learning with Blockchain for Autonomous Vehicles: Analysis and Design Challenges". IEEE Transactions on Communications. 68 (8): 4734–4746. doi:10.1109/TCOMM.2020.2990686. S2CID 219006840.
Xu, Zirui; Yu, Fuxun; Xiong, Jinjun; Chen, Xiang (December 2021). "Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration". 2021 58th ACM/IEEE Design Automation Conference (DAC). pp. 997–1002. arXiv:1912.01684. doi:10.1109/DAC18074.2021.9586241. ISBN 978-1-6654-3274-0. S2CID 243925551.
Yu, Fuxun; Zhang, Weishan; Qin, Zhuwei; Xu, Zirui; Wang, Di; Liu, Chenchen; Tian, Zhi; Chen, Xiang (2021-08-14). "Fed2". Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. KDD '21. New York, NY, USA: Association for Computing Machinery. pp. 2066–2074. arXiv:2111.14248. doi:10.1145/3447548.3467309. ISBN 978-1-4503-8332-5. S2CID 240598436.
Savazzi, Stefano; Nicoli, Monica; Rampa, Vittorio (May 2020). "Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks". IEEE Internet of Things Journal. 7 (5): 4641–4654. arXiv:1912.13163. doi:10.1109/JIOT.2020.2964162. S2CID 209515403.
Reddi, Sashank; Charles, Zachary; Zaheer, Manzil; Garrett, Zachary; Rush, Keith; Konečný, Jakub; Kumar, Sanjiv; McMahan, H. Brendan (2021-09-08), Adaptive Federated Optimization, doi:10.48550/arXiv.2003.00295, retrieved 2024-07-24
Du, Zhiyong; Deng, Yansha; Guo, Weisi; Nallanathan, Arumugam; Wu, Qihui (2021). "Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression, and Challenges". IEEE Vehicular Technology Magazine. 16: 29–39. doi:10.1109/MVT.2020.3015184. hdl:1826/16378. S2CID 204401715.
Pokhrel, Shiva Raj (2020). "Federated learning meets blockchain at 6G edge: a drone-assisted networking for disaster response": 49–54. doi:10.1145/3414045.3415949. S2CID 222179104. {{cite journal}}: Cite journal requires |journal= (help)
Cioffi, Raffaele; Travaglioni, Marta; Piscitelli, Giuseppina; Petrillo, Antonella; De Felice, Fabio (2019). "Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions". Sustainability. 12 (2): 492. doi:10.3390/su12020492.
Putra, Karisma Trinanda; Chen, Hsing-Chung; Prayitno; Ogiela, Marek R.; Chou, Chao-Lung; Weng, Chien-Erh; Shae, Zon-Yin (January 2021). "Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications". Sensors. 21 (13): 4586. Bibcode:2021Senso..21.4586P. doi:10.3390/s21134586. PMC 8271576. PMID 34283140.
Rieke, Nicola; Hancox, Jonny; Li, Wenqi; Milletarì, Fausto; Roth, Holger R.; Albarqouni, Shadi; Bakas, Spyridon; Galtier, Mathieu N.; Landman, Bennett A.; Maier-Hein, Klaus; Ourselin, Sébastien; Sheller, Micah; Summers, Ronald M.; Trask, Andrew; Xu, Daguang; Baust, Maximilian; Cardoso, M. Jorge (14 September 2020). "The future of digital health with federated learning". npj Digital Medicine. 3 (1): 119. arXiv:2003.08119. doi:10.1038/s41746-020-00323-1. PMC 7490367. PMID 33015372. S2CID 212747909.
Dayan, Ittai; Roth, Holger R.; Zhong, Aoxiao; et al. (2021). "Federated learning for predicting clinical outcomes in patients with COVID-19". Nature Medicine. 27 (10): 1735–1743. doi:10.1038/s41591-021-01506-3. PMC 9157510. PMID 34526699. S2CID 237536154.
Prayitno; Shyu, Chi-Ren; Putra, Karisma Trinanda; Chen, Hsing-Chung; Tsai, Yuan-Yu; Hossain, K. S. M. Tozammel; Jiang, Wei; Shae, Zon-Yin (January 2021). "A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications". Applied Sciences. 11 (23): 11191. doi:10.3390/app112311191.
Karargyris, Alexandros; Umeton, Renato; Sheller, Micah J.; et al. (17 July 2023). "Federated benchmarking of medical artificial intelligence with MedPerf". Nature Machine Intelligence. 5 (7). Springer Science and Business Media LLC: 799–810. arXiv:2110.01406. doi:10.1038/s42256-023-00652-2. ISSN 2522-5839. PMC 11068064. PMID 38706981.
Liu, Boyi; Wang, Lujia; Liu, Ming (2019). "Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems". 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 1688–1695. arXiv:1901.06455. doi:10.1109/IROS40897.2019.8967908. ISBN 978-1-7281-4004-9. S2CID 210972473.
Na, Seongin; Rouček, Tomáš; Ulrich, Jiří; Pikman, Jan; Krajník, Tomáš; Lennox, Barry; Arvin, Farshad (2023). "Federated Reinforcement Learning for Collective Navigation of Robotic Swarms". IEEE Transactions on Cognitive and Developmental Systems. 15 (4): 1. arXiv:2202.01141. doi:10.1109/TCDS.2023.3239815. S2CID 246473085.
Yu, Xianjia; Queralta, Jorge Pena; Westerlund, Tomi (2022). "Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer". Procedia Computer Science. 210: 86–93. arXiv:2205.15496. doi:10.1016/j.procs.2022.10.123.

handle.net

hdl.handle.net

Du, Zhiyong; Deng, Yansha; Guo, Weisi; Nallanathan, Arumugam; Wu, Qihui (2021). "Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression, and Challenges". IEEE Vehicular Technology Magazine. 16: 29–39. doi:10.1109/MVT.2020.3015184. hdl:1826/16378. S2CID 204401715.

harvard.edu

ui.adsabs.harvard.edu

Putra, Karisma Trinanda; Chen, Hsing-Chung; Prayitno; Ogiela, Marek R.; Chou, Chao-Lung; Weng, Chien-Erh; Shae, Zon-Yin (January 2021). "Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications". Sensors. 21 (13): 4586. Bibcode:2021Senso..21.4586P. doi:10.3390/s21134586. PMC 8271576. PMID 34283140.

ieee.org

ieeexplore.ieee.org

Xu, Zirui; Yu, Fuxun; Xiong, Jinjun; Chen, Xiang (December 2021). "Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration". 2021 58th ACM/IEEE Design Automation Conference (DAC). pp. 997–1002. arXiv:1912.01684. doi:10.1109/DAC18074.2021.9586241. ISBN 978-1-6654-3274-0. S2CID 243925551.
Du, Zhiyong; Deng, Yansha; Guo, Weisi; Nallanathan, Arumugam; Wu, Qihui (2021). "Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression, and Challenges". IEEE Vehicular Technology Magazine. 16: 29–39. doi:10.1109/MVT.2020.3015184. hdl:1826/16378. S2CID 204401715.

mlcommons.org

"Announcing MedPerf Open Benchmarking Platform for Medical AI". MLCommons. 2023-07-17. Retrieved 2023-09-13.

mlr.press

proceedings.mlr.press

Hsieh, Kevin; Phanishayee, Amar; Mutlu, Onur; Gibbons, Phillip (2020-11-21). "The Non-IID Data Quagmire of Decentralized Machine Learning". International Conference on Machine Learning. PMLR: 4387–4398. arXiv:1910.00189.

nature.com

"Random sketch learning for deep neural networks in edge computing". Nature Computational Science. 1. 2021.

neurips.cc

proceedings.neurips.cc

Xian, Xun; Wang, Xinran; Ding, Jie; Ghanadan, Reza (2020). "Assisted Learning: A Framework for Multi-Organization Learning". Advances in Neural Information Processing Systems. 33. arXiv:2004.00566.

neuroquantology.com

Ismail, Hatem (August 2022). "A FEDERATED PURE VISION TRANSFORMER ALGORITHM FOR COMPUTER VISION USING DYNAMIC AGGREGATION MODEL" (PDF). NeuroQuantology (published Aug 2022).

nih.gov

ncbi.nlm.nih.gov

Putra, Karisma Trinanda; Chen, Hsing-Chung; Prayitno; Ogiela, Marek R.; Chou, Chao-Lung; Weng, Chien-Erh; Shae, Zon-Yin (January 2021). "Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications". Sensors. 21 (13): 4586. Bibcode:2021Senso..21.4586P. doi:10.3390/s21134586. PMC 8271576. PMID 34283140.
Rieke, Nicola; Hancox, Jonny; Li, Wenqi; Milletarì, Fausto; Roth, Holger R.; Albarqouni, Shadi; Bakas, Spyridon; Galtier, Mathieu N.; Landman, Bennett A.; Maier-Hein, Klaus; Ourselin, Sébastien; Sheller, Micah; Summers, Ronald M.; Trask, Andrew; Xu, Daguang; Baust, Maximilian; Cardoso, M. Jorge (14 September 2020). "The future of digital health with federated learning". npj Digital Medicine. 3 (1): 119. arXiv:2003.08119. doi:10.1038/s41746-020-00323-1. PMC 7490367. PMID 33015372. S2CID 212747909.
Dayan, Ittai; Roth, Holger R.; Zhong, Aoxiao; et al. (2021). "Federated learning for predicting clinical outcomes in patients with COVID-19". Nature Medicine. 27 (10): 1735–1743. doi:10.1038/s41591-021-01506-3. PMC 9157510. PMID 34526699. S2CID 237536154.
Karargyris, Alexandros; Umeton, Renato; Sheller, Micah J.; et al. (17 July 2023). "Federated benchmarking of medical artificial intelligence with MedPerf". Nature Machine Intelligence. 5 (7). Springer Science and Business Media LLC: 799–810. arXiv:2110.01406. doi:10.1038/s42256-023-00652-2. ISSN 2522-5839. PMC 11068064. PMID 38706981.

pubmed.ncbi.nlm.nih.gov

Putra, Karisma Trinanda; Chen, Hsing-Chung; Prayitno; Ogiela, Marek R.; Chou, Chao-Lung; Weng, Chien-Erh; Shae, Zon-Yin (January 2021). "Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications". Sensors. 21 (13): 4586. Bibcode:2021Senso..21.4586P. doi:10.3390/s21134586. PMC 8271576. PMID 34283140.
Rieke, Nicola; Hancox, Jonny; Li, Wenqi; Milletarì, Fausto; Roth, Holger R.; Albarqouni, Shadi; Bakas, Spyridon; Galtier, Mathieu N.; Landman, Bennett A.; Maier-Hein, Klaus; Ourselin, Sébastien; Sheller, Micah; Summers, Ronald M.; Trask, Andrew; Xu, Daguang; Baust, Maximilian; Cardoso, M. Jorge (14 September 2020). "The future of digital health with federated learning". npj Digital Medicine. 3 (1): 119. arXiv:2003.08119. doi:10.1038/s41746-020-00323-1. PMC 7490367. PMID 33015372. S2CID 212747909.
Dayan, Ittai; Roth, Holger R.; Zhong, Aoxiao; et al. (2021). "Federated learning for predicting clinical outcomes in patients with COVID-19". Nature Medicine. 27 (10): 1735–1743. doi:10.1038/s41591-021-01506-3. PMC 9157510. PMID 34526699. S2CID 237536154.
Karargyris, Alexandros; Umeton, Renato; Sheller, Micah J.; et al. (17 July 2023). "Federated benchmarking of medical artificial intelligence with MedPerf". Nature Machine Intelligence. 5 (7). Springer Science and Business Media LLC: 799–810. arXiv:2110.01406. doi:10.1038/s42256-023-00652-2. ISSN 2522-5839. PMC 11068064. PMID 38706981.

nowpublishers.com

Kairouz, Peter; McMahan, H. Brendan; Avent, Brendan; Bellet, Aurélien; Bennis, Mehdi; Bhagoji, Arjun Nitin; Bonawitz, Kallista; Charles, Zachary; Cormode, Graham; Cummings, Rachel; D’Oliveira, Rafael G. L.; Eichner, Hubert; Rouayheb, Salim El; Evans, David; Gardner, Josh (2021-06-22). "Advances and Open Problems in Federated Learning". Foundations and Trends in Machine Learning. 14 (1–2): 1–210. arXiv:1912.04977. doi:10.1561/2200000083. ISSN 1935-8237.

semanticscholar.org

api.semanticscholar.org

Pokhrel, Shiva Raj; Choi, Jinho (2020). "Federated Learning with Blockchain for Autonomous Vehicles: Analysis and Design Challenges". IEEE Transactions on Communications. 68 (8): 4734–4746. doi:10.1109/TCOMM.2020.2990686. S2CID 219006840.
Xu, Zirui; Yu, Fuxun; Xiong, Jinjun; Chen, Xiang (December 2021). "Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration". 2021 58th ACM/IEEE Design Automation Conference (DAC). pp. 997–1002. arXiv:1912.01684. doi:10.1109/DAC18074.2021.9586241. ISBN 978-1-6654-3274-0. S2CID 243925551.
Yu, Fuxun; Zhang, Weishan; Qin, Zhuwei; Xu, Zirui; Wang, Di; Liu, Chenchen; Tian, Zhi; Chen, Xiang (2021-08-14). "Fed2". Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. KDD '21. New York, NY, USA: Association for Computing Machinery. pp. 2066–2074. arXiv:2111.14248. doi:10.1145/3447548.3467309. ISBN 978-1-4503-8332-5. S2CID 240598436.
Savazzi, Stefano; Nicoli, Monica; Rampa, Vittorio (May 2020). "Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks". IEEE Internet of Things Journal. 7 (5): 4641–4654. arXiv:1912.13163. doi:10.1109/JIOT.2020.2964162. S2CID 209515403.
Du, Zhiyong; Deng, Yansha; Guo, Weisi; Nallanathan, Arumugam; Wu, Qihui (2021). "Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression, and Challenges". IEEE Vehicular Technology Magazine. 16: 29–39. doi:10.1109/MVT.2020.3015184. hdl:1826/16378. S2CID 204401715.
Pokhrel, Shiva Raj (2020). "Federated learning meets blockchain at 6G edge: a drone-assisted networking for disaster response": 49–54. doi:10.1145/3414045.3415949. S2CID 222179104. {{cite journal}}: Cite journal requires |journal= (help)
Rieke, Nicola; Hancox, Jonny; Li, Wenqi; Milletarì, Fausto; Roth, Holger R.; Albarqouni, Shadi; Bakas, Spyridon; Galtier, Mathieu N.; Landman, Bennett A.; Maier-Hein, Klaus; Ourselin, Sébastien; Sheller, Micah; Summers, Ronald M.; Trask, Andrew; Xu, Daguang; Baust, Maximilian; Cardoso, M. Jorge (14 September 2020). "The future of digital health with federated learning". npj Digital Medicine. 3 (1): 119. arXiv:2003.08119. doi:10.1038/s41746-020-00323-1. PMC 7490367. PMID 33015372. S2CID 212747909.
Dayan, Ittai; Roth, Holger R.; Zhong, Aoxiao; et al. (2021). "Federated learning for predicting clinical outcomes in patients with COVID-19". Nature Medicine. 27 (10): 1735–1743. doi:10.1038/s41591-021-01506-3. PMC 9157510. PMID 34526699. S2CID 237536154.
Liu, Boyi; Wang, Lujia; Liu, Ming (2019). "Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems". 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 1688–1695. arXiv:1901.06455. doi:10.1109/IROS40897.2019.8967908. ISBN 978-1-7281-4004-9. S2CID 210972473.
Na, Seongin; Rouček, Tomáš; Ulrich, Jiří; Pikman, Jan; Krajník, Tomáš; Lennox, Barry; Arvin, Farshad (2023). "Federated Reinforcement Learning for Collective Navigation of Robotic Swarms". IEEE Transactions on Cognitive and Developmental Systems. 15 (4): 1. arXiv:2202.01141. doi:10.1109/TCDS.2023.3239815. S2CID 246473085.

worldcat.org

Kairouz, Peter; McMahan, H. Brendan; Avent, Brendan; Bellet, Aurélien; Bennis, Mehdi; Bhagoji, Arjun Nitin; Bonawitz, Kallista; Charles, Zachary; Cormode, Graham; Cummings, Rachel; D’Oliveira, Rafael G. L.; Eichner, Hubert; Rouayheb, Salim El; Evans, David; Gardner, Josh (2021-06-22). "Advances and Open Problems in Federated Learning". Foundations and Trends in Machine Learning. 14 (1–2): 1–210. arXiv:1912.04977. doi:10.1561/2200000083. ISSN 1935-8237.
Vahid, Diao; Ding, Enmao; Tarokh, Jie (2021-06-02). SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients. OCLC 1269554828.
Karargyris, Alexandros; Umeton, Renato; Sheller, Micah J.; et al. (17 July 2023). "Federated benchmarking of medical artificial intelligence with MedPerf". Nature Machine Intelligence. 5 (7). Springer Science and Business Media LLC: 799–810. arXiv:2110.01406. doi:10.1038/s42256-023-00652-2. ISSN 2522-5839. PMC 11068064. PMID 38706981.