Fetching the paper…
Reading the bibliography…
Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data.
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, K. A. Bonawitz, et al · 1912
Earlier work this paper cites.
Agnostic active learning
Maria-Florina Balcan, Alina Beygelzimer, and John Langford. 2009 · 2009
Earlier work this paper cites.
Curriculum learning. In Proceedings of the 26th annual international conference on machine learning . 41–48
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
Earlier work this paper cites.
Active learning literature survey
Burr Settles. 2009 · 2009
Earlier work this paper cites.
A public domain dataset for human activity recognition using smartphones.. In 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (ESANN’13)
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz, et al · 2013
Earlier work this paper cites.
Derivative-free optimization: a review of algorithms and comparison of software implementations
Luis Miguel Rios and Nikolaos V Sahinidis. 2013 · 2013
Earlier work this paper cites.
The complete works of William Shakespeare
William Shakespeare. 2014 · 2014
Earlier work this paper cites.
Variance reduction in sgd by distributed importance sampling
Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron Courville, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Deep Learning Face Attributes in the Wild. In Proceedings of International Conference on Computer Vision (ICCV)
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015 · 2015
Earlier work this paper cites.
Online batch selection for faster training of neural networks
Ilya Loshchilov and Frank Hutter. 2015 · 2015
Earlier work this paper cites.
Deep Learning with Differential Privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (Vienna, Austria) (CCS ’16) . Association for Computing Machinery, New York, NY, USA, 308–318
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2016 · 2016
Earlier work this paper cites.
EMNIST: Extending MNIST to handwritten letters. In 2017 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2921–2926
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik. 2017 · 2017
Earlier work this paper cites.
Deep bayesian active learning with image data. In International Conference on Machine Learning . PMLR, 1183–1192
Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017 · 2017
Earlier work this paper cites.
Automated curriculum learning for neural networks. In international conference on machine learning . PMLR, 1311–1320
Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu. 2017 · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA (Proceedings of Machine Learning Research, Vol. 54) , Aarti Singh and Xiaojin (Jerry) Zhu (Eds.). PMLR, 1273–1282
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017 · 2017
Earlier work this paper cites.
Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018 · 2018
Earlier work this paper cites.
Not all samples are created equal: Deep learning with importance sampling. In International conference on machine learning . PMLR, 2525–2534
Angelos Katharopoulos and François Fleuret. 2018 · 2018
Earlier work this paper cites.
Learning Differentially Private Recurrent Language Models. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018 · 2018
Earlier work this paper cites.
A smoother way to train structured prediction models
Venkata K Pillutla, Vincent Roulet, Sham M Kakade, and Zaid Harchaoui. 2018 · 2018
Earlier work this paper cites.
Human activity recognition using federated learning. In 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) . IEEE, 1103–1111
Konstantin Sozinov, Vladimir Vlassov, and Sarunas Girdzijauskas. 2018 · 2018
Earlier work this paper cites.
Applied federated learning: Improving google keyboard query suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays. 2018 · 2018
Earlier work this paper cites.
Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018 · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
On the power of curriculum learning in training deep networks. In International Conference on Machine Learning . PMLR, 2535–2544
Guy Hacohen and Daphna Weinshall. 2019 · 2019
Cited alongside, same era.
Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan. 2019 · 2019
Cited alongside, same era.
Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal. 2019 · 2019
Cited alongside, same era.
Federated Learning with Matched Averaging. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, and Yasaman Khazaeni. 2020b · 2020
Later among the works it cites.
Federated learning with differential privacy: Algorithms and performance analysis
Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H Yang, Farhad Farokhi, Shi Jin, Tony QS Quek, and H Vincent Poor. 2020 · 2020
Later among the works it cites.
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
Qiong Wu, Xu Chen, Zhi Zhou, and Junshan Zhang. 2020 · 2020
Later among the works it cites.
On the Latency Variability of Deep Neural Networks for Mobile Inference. In Workshop on Hot Topics in Edge Computing (1-page Poster)
Luting Yang, Bingqian Lu, and Shaolei Ren. 2020 · 2020
Later among the works it cites.
On the Impact of Device and Behavioral Heterogeneity in Federated Learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Smartpc: Hierarchical pace control in real-time federated learning system. In 2019 IEEE Real-Time Systems Symposium (RTSS) . IEEE, 406–418
Li Li, Haoyi Xiong, Zhishan Guo, Jun Wang, and Cheng-Zhong Xu. 2019b · 2019
Cited alongside, same era.
Client selection for federated learning with heterogeneous resources in mobile edge. In ICC 2019-2019 IEEE International Conference on Communications (ICC) . IEEE, 1–7
Takayuki Nishio and Ryo Yonetani. 2019 · 2019
Cited alongside, same era.
Learning with Bad Training Data via Iterative Trimmed Loss Minimization. In ICML . 5739–5748
Yanyao Shen and Sujay Sanghavi. 2019 · 2019
Cited alongside, same era.
Selfie: Refurbishing unclean samples for robust deep learning. In ICML . 5907–5915
Hwanjun Song, Minseok Kim, and Jae-Gil Lee. 2019 · 2019
Cited alongside, same era.
Exploiting unlabeled data in smart cities using federated edge learning. In 2020 International Wireless Communications and Mobile Computing (IWCMC) . IEEE, 1666–1671
Abdullatif Albaseer, Bekir Sait Ciftler, Mohamed Abdallah, and Ala Al-Fuqaha. 2020 · 2020
Cited alongside, same era.
Flower: A Friendly Federated Learning Research Framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D Lane. 2020 · 2020
Cited alongside, same era.
Dynamic sample selection for federated learning with heterogeneous data in fog computing. In ICC 2020-2020 IEEE International Conference on Communications (ICC) . IEEE, 1–6
Lingshuang Cai, Di Lin, Jiale Zhang, and Shui Yu. 2020 · 2020
Cited alongside, same era.
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning. In 2020 54th Asilomar Conference on Signals, Systems, and Computers . IEEE, 1066–1069
Yae Jee Cho, Samarth Gupta, Gauri Joshi, and Osman Yağan. 2020a · 2020
Cited alongside, same era.
Ahmed M. Abdelmoniem, Chen-Yu Ho, Pantelis Papageorgiou, Muhammad Bilal, and Marco Canini. 2021 · 2021
Later among the works it cites.
Federated learning for predicting clinical outcomes in patients with COVID-19
Ittai Dayan, Holger R Roth, Aoxiao Zhong, Ahmed Harouni, Amilcare Gentili, Anas Z Abidin, Andrew Liu, Anthony Beardsworth Costa, Bradford J Wood, Chien-Sung Tsai, et al · 2021
Later among the works it cites.
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net
Enmao Diao, Jie Ding, and Vahid Tarokh. 2021 · 2021
Later among the works it cites.
In-network Computation for Large-scale Federated Learning over Wireless Edge Networks
Thinh Quang Dinh, Diep N Nguyen, Dinh Thai Hoang, Pham Tran Vu, and Eryk Dutkiewicz. 2021 · 2021
Later among the works it cites.
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. In Thirty-Fifth Conference on Neural Information Processing Systems
Samuel Horváth, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, and Nicholas Donald Lane. 2021 · 2021
Later among the works it cites.
Memory-aware curriculum federated learning for breast cancer classification
Amelia Jiménez-Sánchez, Mickael Tardy, Miguel A González Ballester, Diana Mateus, and Gemma Piella. 2021 · 2021
Later among the works it cites.
Oort: Efficient Federated Learning via Guided Participant Selection. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI’21) . 19–35
Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. 2021 · 2021
Later among the works it cites.
Federated Continuous Learning With Broad Network Architecture
Junqing Le, Xinyu Lei, Nankun Mu, Hengrun Zhang, Kai Zeng, and Xiaofeng Liao. 2021 · 2021
Later among the works it cites.
Sample-level Data Selection for Federated Learning. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications . IEEE, 1–10
Anran Li, Lan Zhang, Juntao Tan, Yaxuan Qin, Junhao Wang, and Xiang-Yang Li. 2021d · 2021
Later among the works it cites.
Federated Learning on Non-IID Data Silos: An Experimental Study
Qinbin Li, Yiqun Diao, Quan Chen, and Bingsheng He. 2021a · 2021
Later among the works it cites.
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Bingyan Liu, Yifeng Cai, Ziqi Zhang, Yuanchun Li, Leye Wang, Ding Li, Yao Guo, and Xiangqun Chen. 2021 · 2021
Later among the works it cites.
ClusterFL: A Similarity-Aware Federated Learning System for Human Activity Recognition. In Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services (Virtual Event, Wisconsin) (MobiSys ’21) . Association for Computing Machinery, New York, NY, USA, 54–66
Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Jianwei Huang, and Guoliang Xing. 2021 · 2021
Later among the works it cites.
Optimal Task Assignment for Heterogeneous Federated Learning Devices. In 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS) . IEEE, 661–670
Laércio Lima Pilla. 2021 · 2021
Later among the works it cites.
Sample Selection for Fair and Robust Training. In NeurIPS
Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh. 2021 · 2021
Later among the works it cites.
FedDL: Federated Learning via Dynamic Layer Sharing for Human Activity Recognition. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems . 15–28
Linlin Tu, Xiaomin Ouyang, Jiayu Zhou, Yuze He, and Guoliang Xing. 2021 · 2021
Later among the works it cites.
Overcoming Noisy and Irrelevant Data in Federated Learning. In 25th International Conference on Pattern Recognition, ICPR 2020, Virtual Event / Milan, Italy, January 10-15, 2021 . IEEE, 5020–5027
Tiffany Tuor, Shiqiang Wang, Bong-Jun Ko, Changchang Liu, and Kin K. Leung. 2020 · 2021
Later among the works it cites.
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data. In Proceedings of the Web Conference 2021 . 935–946
Chengxu Yang, Qipeng Wang, Mengwei Xu, Zhenpeng Chen, Kaigui Bian, Yunxin Liu, and Xuanzhe Liu. 2021 · 2021
Later among the works it cites.
Online Coreset Selection for Rehearsal-based Continual Learning
Jaehong Yoon, Divyam Madaan, Eunho Yang, and Sung Ju Hwang. 2021 · 2021
Later among the works it cites.
Protocol Buffers
[n.d.] · 2022
Closest in time.
TensorFlow Lite
[n.d.] · 2022
Closest in time.
FORML: Learning to Reweight Data for Fairness
Bobby Yan, Skyler Seto, and Nicholas Apostoloff. 2022 · 2022
Closest in time.