Fetching the paper…
Reading the bibliography…
Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally-computed models without exposing their raw data.
Frequency principle: Fourier analysis sheds light on deep neural networks
Xu, Z.-Q. J.; Zhang, Y.; Luo, T.; Xiao, Y.; and Ma, Z. 2019 · 1901
Earlier work this paper cites.
Towards federated learning at scale: System design
Bonawitz, K.; Eichner, H.; Grieskamp, W.; Huba, D.; Ingerman, A.; Ivanov, V.; Kiddon, C.; Konečnỳ, J.; Mazzocchi, S.; McMahan, H. B.; et al. 2019 · 1902
Earlier work this paper cites.
Detailed comparison of communication efficiency of split learning and federated learning
Singh, A.; Vepakomma, P.; Gupta, O.; and Raskar, R. 2019 · 1909
Earlier work this paper cites.
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Karimireddy, S. P.; Kale, S.; Mohri, M.; Reddi, S. J.; Stich, S. U.; and Suresh, A. T. 2019 · 1910
Earlier work this paper cites.
Long short-term memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
Earlier work this paper cites.
MNIST dataset
1998 · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
Earlier work this paper cites.
Adaptive personalized federated learning
Deng, Y.; Kamani, M. M.; and Mahdavi, M. 2020 · 2003
Earlier work this paper cites.
Fetchsgd: Communication-efficient federated learning with sketching
Rothchild, D.; Panda, A.; Ullah, E.; Ivkin, N.; Stoica, I.; Braverman, V.; Gonzalez, J.; and Arora, R. 2020 · 2007
Earlier work this paper cites.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Wang, J.; Liu, Q.; Liang, H.; Joshi, G.; and Poor, H. V. 2020b · 2007
Earlier work this paper cites.
Cifar-10 dataset
2010 · 2010
Earlier work this paper cites.
HeteroFL: Computation and communication efficient federated learning for heterogeneous clients
Diao, E.; Ding, J.; and Tarokh, V. 2020 · 2010
Earlier work this paper cites.
Oort: Efficient federated learning via guided participant selection
Lai, F.; Zhu, X.; Madhyastha, H. V.; and Chowdhury, M. 2020 · 2010
Earlier work this paper cites.
Mitigating bias in federated learning
Abay, A.; Zhou, Y.; Baracaldo, N.; Rajamoni, S.; Chuba, E.; and Ludwig, H. 2020 · 2012
Earlier work this paper cites.
Personalized Federated Learning with First Order Model Optimization
Zhang, M.; Sapra, K.; Fidler, S.; Yeung, S.; and Alvarez, J. M. 2020 · 2012
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
Cited alongside, same era.
Fashion MNIST
2015 · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Konečnỳ, J.; McMahan, H. B.; Yu, F. X.; Richtárik, P.; Suresh, A. T.; and Bacon, D. 2016 · 2016
Cited alongside, same era.
Coded federated learning
Dhakal, S.; Prakash, S.; Yona, Y.; Talwar, S.; and Himayat, N. 2019 · 2019
Later among the works it cites.
Cmfl: Mitigating communication overhead for federated learning
Luping, W.; Wei, W.; and Bo, L. 2019 · 2019
Later among the works it cites.
Client selection for federated learning with heterogeneous resources in mobile edge
Nishio, T.; and Yonetani, R. 2019 · 2019
Later among the works it cites.
On the spectral bias of neural networks
Rahaman, N.; Baratin, A.; Arpit, D.; Draxler, F.; Lin, M.; Hamprecht, F.; Bengio, Y.; and Courville, A. 2019 · 2019
Later among the works it cites.
Robust and communication-efficient federated learning from non-iid data
Sattler, F.; Wiedemann, S.; Müller, K.-R.; and Samek, W. 2019 · 2019
Later among the works it cites.
DNN Training with CoreML
2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Shakespeare dataset
2017 · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
Cited alongside, same era.
Federated multi-task learning
Smith, V.; Chiang, C.-K.; Sanjabi, M.; and Talwalkar, A. S. 2017 · 2017
Cited alongside, same era.
Value-decomposition networks for cooperative multi-agent learning
Sunehag, P.; Lever, G.; Gruslys, A.; Czarnecki, W. M.; Zambaldi, V.; Jaderberg, M.; Lanctot, M.; Sonnerat, N.; Leibo, J. Z.; Tuyls, K.; et al. 2017 · 2017
Cited alongside, same era.
Learning attentional communication for multi-agent cooperation
Jiang, J.; and Lu, Z. 2018 · 2018
Cited alongside, same era.
Federated learning with non-iid data
Zhao, Y.; Li, M.; Lai, L.; Suda, N.; Civin, D.; and Chandra, V. 2018 · 2018
Cited alongside, same era.
DNN Training with Tensorflow Lite
2019 · 2019
Cited alongside, same era.
Federated optimization in heterogeneous networks
Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2020 · 2020
Later among the works it cites.
Optimize scheduling of federated learning on battery-powered mobile devices
Wang, C.; Wei, X.; and Zhou, P. 2020 · 2020
Later among the works it cites.
Optimizing federated learning on non-iid data with reinforcement learning
Wang, H.; Kaplan, Z.; Niu, D.; and Li, B. 2020a · 2020
Later among the works it cites.
FedDNA: Federated Learning with Decoupled Normalization-Layer Aggregation for Non-IID Data
Duan, J.-H.; Li, W.; and Lu, S. 2021 · 2021
Later among the works it cites.
Fraboni, Y.; Vidal, R.; Kameni, L.; and Lorenzi, M. 2021 · 2021
Later among the works it cites.
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Yang, H.; Fang, M.; and Liu, J. 2021 · 2021
Later among the works it cites.
Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Reisizadeh, A.; Mokhtari, A.; Hassani, H.; Jadbabaie, A.; and Pedarsani, R. 2020 · 2031
Closest in time.