Fetching the paper…
Reading the bibliography…
Federated Learning (FL) offers a collaborative training framework, allowing multiple clients to contribute to a shared model without compromising data privacy.
Mime: Mimicking centralized stochastic algorithms in federated learning
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2008
Earlier work this paper cites.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2013
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.
Implicit regularization in matrix factorization
Suriya Gunasekar, Blake E Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nati Srebro · 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.
On the optimization of deep networks: Implicit acceleration by overparameterization
Sanjeev Arora, Nadav Cohen, and Elad Hazan · 2018
Earlier work this paper cites.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder · 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.
Implicit regularization of discrete gradient dynamics in linear neural networks
Gauthier Gidel, Francis Bach, and Simon Lacoste-Julien · 2019
Earlier work this paper cites.
Feddane: A federated newton-type method
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smithy · 2019
Earlier work this paper cites.
Distilling bert into simple neural networks with unlabeled transfer data
Subhabrata Mukherjee and Ahmed Hassan Awadallah · 2019
Earlier work this paper cites.
Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
Earlier work this paper cites.
The non-iid data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip Gibbons · 2020
Earlier work this paper cites.
Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2020
Earlier work this paper cites.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Earlier work this paper cites.
Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
Cited alongside, same era.
Effective federated adaptive gradient methods with non-iid decentralized data
Qianqian Tong, Guannan Liang, and Jinbo Bi · 2020
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
Cited alongside, same era.
Haiyang Yu, Ningyu Zhang, Shumin Deng, Zonggang Yuan, Yantao Jia, and Huajun Chen · 2020
Cited alongside, same era.
Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama · 2021
Fedaug: Reducing the local learning bias improves federated learning on heterogeneous data
Yongxin Guo, Tao Lin, and Xiaoying Tang · 2022
Later among the works it cites.
Fedexp: Speeding up federated averaging via extrapolation
Divyansh Jhunjhunwala, Shiqiang Wang, and Gauri Joshi · 2022
Later among the works it cites.
Understanding dimensional collapse in contrastive self-supervised learning
Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian · 2022
Later among the works it cites.
Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang · 2022
Later among the works it cites.
Partial variance reduction improves non-convex federated learning on heterogeneous data
Bo Li, Mikkel N Schmidt, Tommy S Alstrøm, and Sebastian U Stich · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Federated learning of user verification models without sharing embeddings
Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, and Max Welling · 2021
Cited alongside, same era.
Fedpara: Low-rank hadamard product for communication-efficient federated learning
Nam Hyeon-Woo, Moon Ye-Bin, and Tae-Hyun Oh · 2021
Cited alongside, same era.
Preservation of the global knowledge by not-true distillation in federated learning
Gihun Lee, Minchan Jeong, Yongjin Shin, Sangmin Bae, and Se-Young Yun · 2021
Cited alongside, same era.
No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, and Jiashi Feng · 2021
Cited alongside, same era.
Fedbabu: Towards enhanced representation for federated image classification
Jaehoon Oh, Sangmook Kim, and Se-Young Yun · 2021
Cited alongside, same era.
The effects of mild over-parameterization on the optimization landscape of shallow relu neural networks
Itay M Safran, Gilad Yehudai, and Ohad Shamir · 2021
Cited alongside, same era.
Understanding self-supervised learning dynamics without contrastive pairs
Yuandong Tian, Xinlei Chen, and Surya Ganguli · 2021
Cited alongside, same era.
Mahdi Morafah, Saeed Vahidian, Chen Chen, Mubarak Shah, and Bill Lin · 2022
Later among the works it cites.
Xinyi Shang, Yang Lu, Gang Huang, and Hanzi Wang · 2022
Later among the works it cites.
Towards understanding and mitigating dimensional collapse in heterogeneous federated learning
Yujun Shi, Jian Liang, Wenqing Zhang, Vincent YF Tan, and Song Bai · 2022
Later among the works it cites.
Fedproto: Federated prototype learning across heterogeneous clients
Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang · 2022
Later among the works it cites.
Virtual homogeneity learning: Defending against data heterogeneity in federated learning
Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, and Xiaowen Chu · 2022
Later among the works it cites.
Federated learning with label distribution skew via logits calibration
Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, and Chao Wu · 2022
Later among the works it cites.
Surgical fine-tuning improves adaptation to distribution shifts
Yoonho Lee, Annie S Chen, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, and Chelsea Finn · 2023
Closest in time.
Improving generalization of adapter-based cross-lingual transfer with scheduled unfreezing
Chen Cecilia Liu, Jonas Pfeiffer, Ivan Vulić, and Iryna Gurevych · 2023
Closest in time.
Personalized federated learning with feature alignment and classifier collaboration
Jian Xu, Xinyi Tong, and Shao-Lun Huang · 2023
Closest in time.
Freeze then train: Towards provable representation learning under spurious correlations and feature noise
Haotian Ye, James Zou, and Linjun Zhang · 2023
Closest in time.