Fetching the paper…
Reading the bibliography…
Federated Learning (FL) is a decentralized learning paradigm, in which multiple clients collaboratively train deep learning models without centralizing their local data, and hence preserve data privacy.
Statistical distance and hilbert space
William K Wootters · 1981
Earlier work this paper cites.
A database for handwritten text recognition research
Jonathan J. Hull · 1994
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Earlier work this paper cites.
Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
Earlier work this paper cites.
Data-free knowledge distillation for deep neural networks
Raphael Gontijo Lopes, Stefano Fenu, and Thad Starner · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Earlier work this paper cites.
Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim · 2018
Earlier work this paper cites.
Generalizing across domains via cross-gradient training
Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, and Sunita Sarawagi · 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
Earlier work this paper cites.
Federated learning with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary · 2019
Earlier work this paper cites.
Domain generalization via model-agnostic learning of semantic features
Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, and Ben Glocker · 2019
Earlier work this paper cites.
Fedmd: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang · 2019
Earlier work this paper cites.
Rxrx1: An image set for cellular morphological variation across many experimental batches
J. Taylor, B. Earnshaw, B. Mabey, M. Victors, and J. Yosinski · 2019
Earlier work this paper cites.
Siloed federated learning for multi-centric histopathology datasets
Mathieu Andreux, Jean Ogier du Terrail, Constance Beguier, and Eric W Tramel · 2020
Earlier work this paper cites.
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
Earlier work this paper cites.
In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
Earlier work this paper cites.
Group knowledge transfer: Federated learning of large cnns at the edge
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
Earlier work this paper cites.
A review of applications in federated learning
Li Li, Yuxi Fan, Mike Tse, and Kuo-Yi Lin · 2020
Earlier work this paper cites.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi · 2020
Cited alongside, same era.
Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen Tran, and Josh Nguyen · 2020
Cited alongside, same era.
A survey of unsupervised deep domain adaptation
Garrett Wilson and Diane J Cook · 2020
Cited alongside, same era.
Learning to generate novel domains for domain generalization
Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, and Tao Xiang · 2020
Cited alongside, same era.
Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2021
Later among the works it cites.
Domain generalization in vision: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2021
Later among the works it cites.
Domain generalization with mixstyle
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang · 2021
Later among the works it cites.
Federated learning on non-iid data: A survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu, and Yaochu Jin · 2021
Later among the works it cites.
Data-free knowledge distillation for heterogeneous federated learning
Zhuangdi Zhu, Junyuan Hong, and Jiayu Zhou · 2021
Later among the works it cites.
Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Exploiting shared representations for personalized federated learning
Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai · 2021
Cited alongside, same era.
An overview of federated deep learning privacy attacks and defensive strategies
David Enthoven and Zaid Al-Ars · 2021
Cited alongside, same era.
Towards data-free domain generalization
Ahmed Frikha, Haokun Chen, Denis Krompaß, Thomas Runkler, and Volker Tresp · 2021
Cited alongside, same era.
Ahmed Frikha, Denis Krompaß, and Volker Tresp · 2021
Cited alongside, same era.
Ensemble attention distillation for privacy-preserving federated learning
Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, and Arun Innanje · 2021
Cited alongside, same era.
Towards fair federated learning with zero-shot data augmentation
Weituo Hao, Mostafa El-Khamy, Jungwon Lee, Jianyi Zhang, Kevin J Liang, Changyou Chen, and Lawrence Carin Duke · 2021
Cited alongside, same era.
Samiul Alam, Luyang Liu, Ming Yan, and Mi Zhang · 2022
Closest in time.
Calfat: Calibrated federated adversarial training with label skewness
Chen Chen, Yuchen Liu, Xingjun Ma, and Lingjuan Lyu · 2022
Closest in time.
Evidential neighborhood contrastive learning for universal domain adaptation
Liang Chen, Yihang Lou, Jianzhong He, Tao Bai, and Minghua Deng · 2022
Closest in time.
Denoised maximum classifier discrepancy for sourcefree unsupervised domain adaptation
Tong Chu, Yahao Liu, Jinhong Deng, Wen Li, and Lixin Duan · 2022
Closest in time.
Implicit gradient alignment in distributed and federated learning
Yatin Dandi, Luis Barba, and Martin Jaggi · 2022
Closest in time.
Preserving privacy in federated learning with ensemble cross-domain knowledge distillation
Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, and Arun Innanje · 2022
Closest in time.
Factorized-fl: Personalized federated learning with parameter factorization & similarity matching
Wonyong Jeong and Sung Ju Hwang · 2022
Closest in time.
Harmofl: Harmonizing local and global drifts in federated learning on heterogeneous medical images
Meirui Jiang, Zirui Wang, and Qi Dou · 2022
Closest in time.
Style neophile: Constantly seeking novel styles for domain generalization
Juwon Kang, Sohyun Lee, Namyup Kim, and Suha Kwak · 2022
Closest in time.
Multiple-source domain adaptation via coordinated domain encoders and paired classifiers
Payam Karisani · 2022
Closest in time.
Multi-level branched regularization for federated learning
Jinkyu Kim, Geeho Kim, and Bohyung Han · 2022
Closest in time.
Invariant information bottleneck for domain generalization
Bo Li, Yifei Shen, Yezhen Wang, Wenzhen Zhu, Dongsheng Li, Kurt Keutzer, and Han Zhao · 2022
Closest in time.
Privacy and robustness in federated learning: Attacks and defenses
Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, and S Yu Philip · 2022
Closest in time.
Local learning matters: Rethinking data heterogeneity in federated learning
Matias Mendieta, Taojiannan Yang, Pu Wang, Minwoo Lee, Zhengming Ding, and Chen Chen · 2022
Closest in time.
Fedsoft: Soft clustered federated learning with proximal local updating
Yichen Ruan and Carlee Joe-Wong · 2022
Closest in time.
Fedproto: Federated prototype learning across heterogeneous clients
Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang · 2022
Closest in time.
Gearnet: Stepwise dual learning for weakly supervised domain adaptation
Renchunzi Xie, Hongxin Wei, Lei Feng, and Bo An · 2022
Closest in time.
Acceleration of federated learning with alleviated forgetting in local training
Chencheng Xu, Zhiwei Hong, Minlie Huang, and Tao Jiang · 2022
Closest in time.
Interpretable domain adaptation for hidden subdomain alignment in the context of pre-trained source models
Luxin Zhang, Pascal Germain, Yacine Kessaci, and Christophe Biernacki · 2022
Closest in time.
Fine-tuning global model via data-free knowledge distillation for non-iid federated learning
Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, and Ling-Yu Duan · 2022
Closest in time.