Fetching the paper…
Reading the bibliography…
Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios.
Visualizing higher-layer features of a deep network
D. Erhan, Y. Bengio, A. Courville, and P. Vincent · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
Earlier work this paper cites.
Image quality metrics: Psnr vs. ssim
A. Hore and D. Ziou · 2010
Earlier work this paper cites.
Cifar-10 (canadian institute for advanced research), 2010
A. Krizhevsky, V. Nair, and G. Hinton · 2010
Earlier work this paper cites.
Stochastic first-and zeroth-order methods for nonconvex stochastic programming
S. Ghadimi and G. Lan · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Earlier work this paper cites.
Tiny imagenet visual recognition challenge
Y. Le and X. Yang · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
X. Zhang, J. J. Zhao, and Y. LeCun · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell · 2016
Earlier work this paper cites.
Qsgd: Communication-efficient sgd via gradient quantization and encoding
D. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovic · 2017
Earlier work this paper cites.
Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
X. Lian, C. Zhang, H. Zhang, C.-J. Hsieh, W. Zhang, and J. Liu · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
Earlier work this paper cites.
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
M. Raghu, J. Gilmer, J. Yosinski, and J. Sohl-Dickstein · 2017
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. M. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Expanding the reach of federated learning by reducing client resource requirements
S. Caldas, J. Konečny, H. B. McMahan, and A. Talwalkar · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction
A. S. Hard, K. Rao, R. Mathews, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage · 2018
Cited alongside, same era.
Federated optimization in heterogeneous networks
A. K. Sahu, T. Li, M. Sanjabi, M. Zaheer, A. Talwalkar, and V. Smith · 2018
Cited alongside, same era.
Federated learning with personalization layers
M. G. Arivazhagan, V. Aggarwal, A. K. Singh, and S. Choudhary · 2019
Cited alongside, same era.
Model-contrastive federated learning
Q. Li, B. He, and D. X. Song · 2021
Later among the works it cites.
Efficient and private federated learning with partially trainable networks
H. Sidahmed, Z. Xu, A. Garg, Y. Cao, and M. Chen · 2021
Later among the works it cites.
Federated learning in edge computing: a systematic survey
H. G. Abreha, M. Hayajneh, and M. A. Serhani · 2022
Later among the works it cites.
Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction
S. Alam, L. Liu, M. Yan, and M. Zhang · 2022
Later among the works it cites.
pfl-bench: A comprehensive benchmark for personalized federated learning
D. Chen, D. Gao, W. Kuang, Y. Li, and B. Ding · 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…
Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. B. Charles, G. Cormode, R. Cummings, R. G. L. D’Oliveira, S. Y. E. Rouayheb, D. Evans, J. Gardner, Z. Garrett, A. Gascón, B. Ghazi, P. B. Gibbons, M. Gruteser, Z. Harchaoui, C. He, L. He, Z. Huo, B. Hutchinson, J. Hsu, M. Jaggi, T. Javidi, G. Joshi, M. Khodak, J. Konecný, A. Korolova, F. Koushanfar, O. Koyejo, T. Lepoint, Y. Liu, P. Mittal, M. Mohri, R. Nock, A. Özgür, R. Pagh, M. Raykova, H. Qi, D. Ramage, R. Raskar, D. X. Song, W. Song, S. U. Stich, Z. Sun, A. T. Suresh, F. Tramèr, P. Vepakomma, J. Wang, L. Xiong, Z. Xu, Q. Yang, F. X. Yu, H. Yu, and S. Zhao · 2019
Cited alongside, same era.
Federated learning: Challenges, methods, and future directions
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith · 2019
Cited alongside, same era.
Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
H. Yu, S. Yang, and S. Zhu · 2019
Cited alongside, same era.
Deep leakage from gradients
L. Zhu, Z. Liu, and S. Han · 2019
Cited alongside, same era.
Heterofl: Computation and communication efficient federated learning for heterogeneous clients
E. Diao, J. Ding, and V. Tarokh · 2020
Cited alongside, same era.
SCAFFOLD: Stochastic controlled averaging for federated learning
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh · 2020
Cited alongside, same era.
Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
J. Rasley, S. Rajbhandari, O. Ruwase, and Y. He · 2020
Cited alongside, same era.
K. Pfeiffer, M. Rapp, R. Khalili, and J. Henkel · 2022
Later among the works it cites.
Towards personalized federated learning
A. Z. Tan, H. Yu, L. Cui, and Q. Yang · 2022
Later among the works it cites.
Progfed: effective, communication, and computation efficient federated learning by progressive training
H.-P. Wang, S. Stich, Y. He, and M. Fritz · 2022
Later among the works it cites.
Partial variable training for efficient on-device federated learning
T.-J. Yang, D. Guliani, F. Beaufays, and G. Motta · 2022
Later among the works it cites.
Svdfed: Enabling communication-efficient federated learning via singular-value-decomposition
H. Wang, X. Liu, J. Niu, and S. Tang · 2023
Later among the works it cites.
Model-heterogeneous federated learning with partial model training
H. Wu, P. Wang, and A. C. Narayan · 2023
Later among the works it cites.
Automatic detection of congestive heart failure based on multiscale residual unet++: From centralized learning to federated learning
L. Zou, Z. Huang, X. Yu, J. Zheng, A. Liu, and M. Lei · 2023
Later among the works it cites.
What’s the backward-forward flop ratio for neural networks?, 2021
M. Hobbhahn and J. Sevilla · 2024
Closest in time.
Coordinate descent
B. Poczos and R. Tibshirani · 2024
Closest in time.