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We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately on the devices.
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
M. McCloskey and N. J. Cohen · 1989
Earlier work this paper cites.
Multitask learning
R. Caruana · 1997
Earlier work this paper cites.
Applied Numerical Linear Algebra
J. W. Demmel · 1997
Earlier work this paper cites.
A Model of Inductive Bias Learning
J. Baxter · 2000
Earlier work this paper cites.
Regularized Multi–Task Learning
T. Evgeniou and M. Pontil · 2004
Earlier work this paper cites.
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber · 2006
Earlier work this paper cites.
A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning
R. Collobert and J. Weston · 2008
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li · 2009
Earlier work this paper cites.
Context dependent recurrent neural network language model
T. Mikolov and G. Zweig · 2012
Earlier work this paper cites.
LibriSpeech: an ASR Corpus based on Public Domain Audio Books
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur · 2015
Earlier work this paper cites.
Deep Learning with Differential Privacy
M. Abadi, A. Chu, I. J. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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.
Cross-stitch Networks for Multi-Task Learning
I. Misra, A. Shrivastava, A. Gupta, and M. Hebert · 2016
Earlier work this paper cites.
On the Convergence of Decentralized Gradient Descent
K. Yuan, Q. Ling, and W. Yin · 2016
Earlier work this paper cites.
EMNIST: an extension of MNIST to handwritten letters
G. Cohen, S. Afshar, J. Tapson, and A. van Schaik · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 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
Cited alongside, same era.
Learning multiple visual domains with residual adapters
S. Rebuffi, H. Bilen, and A. Vedaldi · 2017
Cited alongside, same era.
Federated Multi-Task Learning
V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar · 2017
Cited alongside, same era.
Attention is All you Need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Optimization Methods for Large-Scale Machine Learning
L. Bottou, F. E. Curtis, and J. Nocedal · 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.
Federated Visual Classification with Real-World Data Distribution
T. H. Hsu, H. Qi, and M. Brown · 2020
Later among the works it cites.
SCAFFOLD: Stochastic controlled averaging for federated learning
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh · 2020
Later among the works it cites.
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
A. Koloskova, N. Loizou, S. Boreiri, M. Jaggi, and S. Stich · 2020
Later among the works it cites.
On the Convergence of FedAvg on Non-IID Data
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang · 2020
Later among the works it cites.
Three Approaches for Personalization with Applications to Federated Learning
Y. Mansour, M. Mohri, J. Ro, and A. T. Suresh · 2020
Later among the works it cites.
Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
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Federated User Representation Learning
D. Bui, K. Malik, J. Goetz, H. Liu, S. Moon, A. Kumar, and K. G. Shin · 2019
Cited alongside, same era.
Parameter-Efficient Transfer Learning for NLP
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. de Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly · 2019
Cited alongside, same era.
Think Locally, Act Globally: Federated Learning with Local and Global Representations
P. P. Liang, T. Liu, Z. Liu, R. Salakhutdinov, and L. Morency · 2019
Cited alongside, same era.
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures
G. Synnaeve, Q. Xu, J. Kahn, T. Likhomanenko, E. Grave, V. Pratap, A. Sriram, V. Liptchinsky, and R. Collobert · 2019
Cited alongside, same era.
Well-read students learn better: On the importance of pre-training compact models
I. Turc, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Cited alongside, same era.
Federated Residual Learning
A. Agarwal, J. Langford, and C. Wei · 2020
Cited alongside, same era.
T. Weyand, A. Araujo, B. Cao, and J. Sim · 2020
Later among the works it cites.
Debiasing Model Updates for Improving Personalized Federated Training
D. A. E. Acar, Y. Zhao, R. Zhu, R. M. Navarro, M. Mattina, P. N. Whatmough, and V. Saligrama · 2021
Later among the works it cites.
Exploiting Shared Representations for Personalized Federated Learning
L. Collins, H. Hassani, A. Mokhtari, and S. Shakkottai · 2021
Later among the works it cites.
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
F. Hanzely, B. Zhao, and M. Kolar · 2021
Later among the works it cites.
Advances and Open Problems in Federated Learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. A. Bonawitz, Z. Charles, G. Cormode, R. Cummings, R. G. L. D’Oliveira, H. Eichner, S. 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. Konečný, A. Korolova, F. Koushanfar, S. Koyejo, T. Lepoint, Y. Liu, P. Mittal, M. Mohri, R. Nock, A. Özgür, R. Pagh, H. Qi, D. Ramage, R. Raskar, M. Raykova, D. 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 · 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.
Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach
K. Pillutla, Y. Laguel, J. Malick, and Z. Harchaoui · 2021
Later among the works it cites.
Adaptive Federated Optimization
S. J. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečný, S. Kumar, and H. B. McMahan · 2021
Later among the works it cites.
Federated reconstruction: Partially local federated learning
K. Singhal, H. Sidahmed, Z. Garrett, S. Wu, K. Rush, and S. Prakash · 2021
Later among the works it cites.
A Field Guide to Federated Optimization
J. Wang, Z. Charles, Z. Xu, G. Joshi, H. B. McMahan, M. Al-Shedivat, G. Andrew, S. Avestimehr, K. Daly, D. Data, et al · 2021
Later among the works it cites.