Fetching the paper…
Reading the bibliography…
Federated learning (FL) is a promising distributed machine learning paradigm that enables multiple clients to collaboratively train a global model.
2016
Earlier work this paper cites.
J. Zhang, Y. Zhou, and C. Zong, “Abstractive cross-language summarization via translation model enhanced predicate argument structure fusing,”
2016
Earlier work this paper cites.
M. Ziemski, M. Junczys-Dowmunt, and B. Pouliquen, “The United Nations parallel corpus v1.0,” in
2016
Earlier work this paper cites.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in
2017
Earlier work this paper cites.
B. Zhang, D. Xiong, J. Su, and H. Duan, “A context-aware recurrent encoder for neural machine translation,”
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in
2017
Earlier work this paper cites.
R. Wang, M. Utiyama, A. M. Finch, L. Liu, K. Chen, and E. Sumita, “Sentence selection and weighting for neural machine translation domain adaptation,”
2018
Earlier work this paper cites.
P. Michel and G. Neubig, “MTNT: A testbed for machine translation of noisy text,” in
2018
Earlier work this paper cites.
M. Post, “A call for clarity in reporting bleu scores,”
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” 2019
2019
Earlier work this paper cites.
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-i.i.d. data,”
2019
Earlier work this paper cites.
O. Kovaleva, A. Romanov, A. Rogers, and A. Rumshisky, “Revealing the dark secrets of bert,” in
2019
Earlier work this paper cites.
S. Cao, C. Zhang, Z. Yao, W. Xiao, L. Nie, D. chen Zhan, Y. Liu, M. Wu, and L. Zhang, “Efficient and effective sparse lstm on fpga with bank-balanced sparsity,”
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
K. Tyagi, S. M. Nguyen, R. Rawat, and M. T. Manry, “Second order training and sizing for the multilayer perceptron,”
2019
Cited alongside, same era.
2019
Cited alongside, same era.
M. Gupta and P. Agrawal, “Compression of deep learning models for text: A survey,”
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Y.-W. Chu, E. Tenorio, L. Cruz, K. A. Douglas, A. S. Lan, and C. G. Brinton, “Click-based student performance prediction: A clustering guided meta-learning approach,”
2021
Later among the works it cites.
O. Weller, M. Marone, V. Braverman, D. Lawrie, and B. Van Durme, “Pretrained models for multilingual federated learning,” in
2022
Later among the works it cites.
P. Passban, T. Roosta, R. Gupta, A. Chadha, and C. Chung, “Training mixed-domain translation models via federated learning,” in
2022
Later among the works it cites.
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.
B. Li, Z. Kong, T. Zhang, J. Li, Z. Li, H. Liu, and C. Ding, “Efficient transformer-based large scale language representations using hardware-friendly block structured pruning,” in
2020
Cited alongside, same era.
S. Ge, F. Wu, C. Wu, T. Qi, Y. Huang, and X. Xie, “Fedner: Medical named entity recognition with federated learning,”
2020
Cited alongside, same era.
R. Rei, C. Stewart, A. C. Farinha, and A. Lavie, “COMET: A neural framework for MT evaluation,” in
2020
Cited alongside, same era.
K. Sinha, P. Parthasarathi, J. Wang, R. Lowe, W. L. Hamilton, and J. Pineau, “Learning an unreferenced metric for online dialogue evaluation,” in
2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. Vadera and S. Ameen, “Methods for pruning deep neural networks,”
2020
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Later among the works it cites.
S. Wang, J. B. Perazzone, M. Ji, and K. S. Chan, “Federated learning with flexible control,”
2022
Later among the works it cites.
Y.-W. Chu, S. Hosseinalipour, E. Tenorio, L. Cruz, K. A. Douglas, A. S. Lan, and C. G. Brinton, “Mitigating biases in student performance prediction via attention-based personalized federated learning,”
2022
Later among the works it cites.
B. Y. Lin, C. He, Z. Ze, H. Wang, Y. Hua, C. Dupuy, R. Gupta, M. Soltanolkotabi, X. Ren, and S. Avestimehr, “FedNLP: Benchmarking federated learning methods for natural language processing tasks,” in
2022
Later among the works it cites.
L. Melas-Kyriazi and F. Wang, “Intrinsic gradient compression for scalable and efficient federated learning,”
2022
Later among the works it cites.
J. Ro, T. Breiner, L. McConnaughey, M. Chen, A. Suresh, S. Kumar, and R. Mathews, “Scaling language model size in cross-device federated learning,” in
2022
Later among the works it cites.
C.-Y. Hsu, Y.-W. Chu, V. Chen, K.-C. Lo, C. Chen, T.-H. K. Huang, and L.-W. Ku, “Learning to rank visual stories from human ranking data,” in
2022
Later among the works it cites.
R. Parasnis, S. Hosseinalipour, Y.-W. Chu, C. G. Brinton, and M. Chiang, “Connectivity-aware semi-decentralized federated learning over time-varying d2d networks,”
2023
Later among the works it cites.
G. Lan, X.-Y. Liu, Y. Zhang, and X. Wang, “Communication-efficient federated learning for resource-constrained edge devices,”
2023
Later among the works it cites.
Y.-W. Chu, D.-J. Han, and C. G. Brinton, “Only send what you need: Learning to communicate efficiently in federated multilingual machine translation,”
2024
Closest in time.
Z. Lu, H. Pan, Y. Dai, X. Si, and Y. Zhang, “Federated learning with non-iid data: A survey,”
2024
Closest in time.
C. Chen, H. C. Xu, W. Wang, B. Li, B. Li, L. Chen, and G. Zhang, “Synchronize only the immature parameters: Communication-efficient federated learning by freezing parameters adaptively,”
2024
Closest in time.
Y.-W. Chu, D.-J. Han, S. Hosseinalipour, and C. G. Brinton, “Rethinking the starting point: Collaborative pre-training for federated downstream tasks,” 2024. [Online]. Available:
2024
Closest in time.