Fetching the paper…
Reading the bibliography…
To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks.
Web 3.0 emerging
J. Hendler · 2009
Earlier work this paper cites.
Learning word vectors for sentiment analysis
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts · 2011
Earlier work this paper cites.
Teaching machines to read and comprehend
K. M. Hermann, T. Kociský, E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, and P. Blunsom · 2015
Earlier work this paper cites.
SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
D. Cer, M. Diab, E. Agirre, I. Lopez-Gazpio, and L. Specia · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 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.
Deep multimodal learning: A survey on recent advances and trends
D. Ramachandram and G. W. Taylor · 2017
Earlier work this paper cites.
Federated multi-task learning
V. Smith, C. Chiang, M. Sanjabi, and A. Talwalkar · 2017
Earlier work this paper cites.
Moleculenet: A benchmark for molecular machine learning
Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. S. Pande · 2017
Earlier work this paper cites.
Leaf: A benchmark for federated settings
S. Caldas, S. M. K. Duddu, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
Earlier work this paper cites.
Federated learning for mobile keyboard prediction
A. Hard, K. Rao, R. Mathews, S. Ramaswamy, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage · 2018
Earlier work this paper cites.
Know what you don’t know: Unanswerable questions for squad
P. Rajpurkar, R. Jia, and P. Liang · 2018
Earlier work this paper cites.
Federated learning with non-iid data
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra · 2018
Earlier work this paper cites.
Towards federated learning at scale: System design
K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konečnỳ, S. Mazzocchi, B. McMahan, et al · 2019
Earlier work this paper cites.
Unified language model pre-training for natural language understanding and generation
L. Dong, N. Yang, W. Wang, F. Wei, X. Liu, Y. Wang, J. Gao, M. Zhou, and H.-W. Hon · 2019
Earlier work this paper cites.
Improving Federated Learning Personalization via Model Agnostic Meta Learning
Y. Jiang, J. Konečný, K. Rush, and S. Kannan · 2019
Earlier work this paper cites.
Adaptive gradient-based meta-learning methods
M. Khodak, M.-F. F. Balcan, and A. S. Talwalkar · 2019
Cited alongside, same era.
Federated learning for keyword spotting
D. Leroy, A. Coucke, T. Lavril, T. Gisselbrecht, and J. Dureau · 2019
Cited alongside, same era.
Fair resource allocation in federated learning
T. Li, M. Sanjabi, A. Beirami, and V. Smith · 2019
Cited alongside, same era.
Continual lifelong learning with neural networks: A review
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter · 2019
Cited alongside, same era.
How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications
Q. Yang, Y. Liu, T. Chen, and Y. Tong · 2019
Cited alongside, same era.
Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
F. Sattler, K.-R. Müller, and W. Samek · 2020
Later among the works it cites.
FedSteg: A Federated Transfer Learning Framework for Secure Image Steganalysis
H. Yang, H. He, W. Zhang, and X. Cao · 2020
Later among the works it cites.
Personalized federated learning with first order model optimization
M. Zhang, K. Sapra, S. Fidler, S. Yeung, and J. M. Alvarez · 2020
Later among the works it cites.
Heterofl: Computation and communication efficient federated learning for heterogeneous clients
E. Diao, J. Ding, and V. Tarokh · 2021
Later among the works it cites.
Fedgraphnn: A federated learning system and benchmark for graph neural networks
C. He, K. Balasubramanian, E. Ceyani, Y. Rong, P. Zhao, J. Huang, M. Annavaram, and S. Avestimehr · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Federated learning
Q. Yang, Y. Liu, Y. Cheng, Y. Kang, T. Chen, and H. Yu · 2019
Cited alongside, same era.
Federated learning with hierarchical clustering of local updates to improve training on non-iid data
C. Briggs, Z. Fan, and P. Andras · 2020
Cited alongside, same era.
Fedeval: A benchmark system with a comprehensive evaluation model for federated learning
D. Chai, L. Wang, K. Chen, and Q. Yang · 2020
Cited alongside, same era.
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
A. Fallah, A. Mokhtari, and A. Ozdaglar · 2020
Cited alongside, same era.
Group knowledge transfer: Federated learning of large cnns at the edge
C. He, M. Annavaram, and S. Avestimehr · 2020
Cited alongside, same era.
Fedml: A research library and benchmark for federated machine learning
C. He, S. Li, J. So, M. Zhang, H. Wang, X. Wang, P. Vepakomma, A. Singh, H. Qiu, L. Shen, P. Zhao, Y. Kang, Y. Liu, R. Raskar, Q. Yang, M. Annavaram, and S. Avestimehr · 2020
Cited alongside, same era.
Fedscale: Benchmarking model and system performance of federated learning
F. Lai, Y. Dai, X. Zhu, H. V. Madhyastha, and M. Chowdhury · 2021
Later among the works it cites.
Ditto: Fair and robust federated learning through personalization
T. Li, S. Hu, A. Beirami, and V. Smith · 2021
Later among the works it cites.
Ditto: Fair and robust federated learning through personalization
T. Li, S. Hu, A. Beirami, and V. Smith · 2021
Later among the works it cites.
Fedbn: Federated learning on non-iid features via local batch normalization
X. Li, M. Jiang, X. Zhang, M. Kamp, and Q. Dou · 2021
Later among the works it cites.
Federated learning for healthcare informatics
J. Xu, B. S. Glicksberg, C. Su, P. Walker, J. Bian, and F. Wang · 2021
Later among the works it cites.
Parameterized knowledge transfer for personalized federated learning
J. Zhang, S. Guo, X. Ma, H. Wang, W. Xu, and F. Wu · 2021
Later among the works it cites.
Federated learning on non-iid data: A survey
H. Zhu, J. Xu, S. Liu, and Y. Jin · 2021
Later among the works it cites.
Data-free knowledge distillation for heterogeneous federated learning
Z. Zhu, J. Hong, and J. Zhou · 2021
Later among the works it cites.
Pysyft: A library for easy federated learning
A. Ziller, A. Trask, A. Lopardo, B. Szymkow, B. Wagner, E. Bluemke, J.-M. Nounahon, J. Passerat-Palmbach, K. Prakash, N. Rose, et al · 2021
Later among the works it cites.
Federatedscope: A comprehensive and flexible federated learning platform via message passing
Y. Xie, Z. Wang, D. Chen, D. Gao, L. Yao, W. Kuang, Y. Li, B. Ding, and J. Zhou · 2022
Closest in time.
Federatedscope: A comprehensive and flexible federated learning platform via message passing
Y. Xie, Z. Wang, D. Chen, D. Gao, L. Yao, W. Kuang, Y. Li, B. Ding, and J. Zhou · 2022
Closest in time.