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Personalized federated learning is proposed to handle the data heterogeneity problem amongst clients by learning dedicated tailored local models for each user.
Li, A., Sun, J., Wang, B., Duan, L., Li, S., Chen, Y., and Li, H · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. et al · 2009
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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Gossip training for deep learning
Blot, M., Picard, D., Cord, M., and Thome, N · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Qsgd: Communication-efficient sgd via gradient quantization and encoding
Alistarh, D., Grubic, D., Li, J., Tomioka, R., and Vojnovic, M · 2017
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Collaborative deep learning in fixed topology networks
Jiang, Z., Balu, A., Hegde, C., and Sarkar, S · 2017
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Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., and Liu, J · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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The eu general data protection regulation (gdpr)
Voigt, P. and Von dem Bussche, A · 2017
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Terngrad: Ternary gradients to reduce communication in distributed deep learning
Wen, W., Xu, C., Yan, F., Wu, C., Wang, Y., Chen, Y., and Li, H · 2017
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Balle, B., Barthe, G., and Gaboardi, M · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J. and Carbin, M · 2018
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Fully decentralized federated learning
Lalitha, A., Shekhar, S., Javidi, T., and Koushanfar, F · 2018
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Rethinking the value of network pruning
Liu, Z., Sun, M., Zhou, T., Huang, G., and Darrell, T · 2018
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., and Liotta, A · 2018
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Foundations of machine learning
Mohri, M., Rostamizadeh, A., and Talwalkar, A · 2018
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Group normalization
Wu, Y. and He, K · 2018
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A convergence theory for deep learning via over-parameterization
Allen-Zhu, Z., Li, Y., and Song, Z · 2019
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Federated learning with personalization layers
Arivazhagan, M. G., Aggarwal, V., Singh, A. K., and Choudhary, S · 2019
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Qsparse-local-sgd: Distributed sgd with quantization, sparsification and local computations
Basu, D., Data, D., Karakus, C., and Diggavi, S · 2019
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Control batch size and learning rate to generalize well: Theoretical and empirical evidence
He, F., Liu, T., and Tao, D · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H., Qi, H., and Brown, M · 2019
Cited alongside, same era.
Communication-efficient distributed sgd with sketching
Ivkin, N., Rothchild, D., Ullah, E., Stoica, I., Arora, R., et al · 2019
Cited alongside, same era.
Improving federated learning personalization via model agnostic meta learning
Jiang, Y., Konečnỳ, J., Rush, K., and Kannan, S · 2019
Cited alongside, same era.
Decentralized stochastic optimization and gossip algorithms with compressed communication
Koloskova, A., Stich, S., and Jaggi, M · 2019
Cited alongside, same era.
Peer-to-peer federated learning on graphs
Lalitha, A., Kilinc, O. C., Javidi, T., and Koushanfar, F · 2019
Personalized federated learning with first order model optimization
Zhang, M., Sapra, K., Fidler, S., Yeung, S., and Alvarez, J. M · 2020
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Federated learning based on dynamic regularization
Acar, D. A. E., Zhao, Y., Navarro, R. M., Mattina, M., Whatmough, P. N., and Saligrama, V · 2021
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Federated dynamic sparse training: Computing less, communicating less, yet learning better
Bibikar, S., Vikalo, H., Wang, Z., and Chen, X · 2021
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On bridging generic and personalized federated learning
Chen, H.-Y. and Chao, W.-L · 2021
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Fine-tuning is fine in federated learning
Cheng, G., Chadha, K., and Duchi, J · 2021
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Cited alongside, same era.
3lc: Lightweight and effective traffic compression for distributed machine learning
Lim, H., Andersen, D. G., and Kaminsky, M · 2019
Cited alongside, same era.
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
Mostafa, H. and Wang, X · 2019
Cited alongside, same era.
Client selection for federated learning with heterogeneous resources in mobile edge
Nishio, T. and Yonetani, R · 2019
Cited alongside, same era.
Exploring fast and communication-efficient algorithms in large-scale distributed networks
Yu, Y., Wu, J., and Huang, J · 2019
Cited alongside, same era.
An improved analysis of training over-parameterized deep neural networks
Zou, D. and Gu, Q · 2019
Cited alongside, same era.
Efficient-adam: Communication-efficient distributed adam with complexity analysis
Chen, C., Shen, L., Huang, H., Liu, W., and Luo, Z.-Q · 2020
Cited alongside, same era.
Fedbe: Making bayesian model ensemble applicable to federated learning
Chen, H.-Y. and Chao, W.-L · 2020
Cited alongside, same era.
Exploiting shared representations for personalized federated learning
Collins, L., Hassani, H., Mokhtari, A., and Shakkottai, S · 2021
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Clustered sampling: Low-variance and improved representativity for clients selection in federated learning
Fraboni, Y., Vidal, R., Kameni, L., and Lorenzi, M · 2021
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Personalized federated learning: A unified framework and universal optimization techniques
Hanzely, F., Zhao, B., and Kolar, M · 2021
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Tighter generalization bounds for iterative differentially private learning algorithms
He, F., Wang, B., and Tao, D · 2021
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Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Horvath, S., Laskaridis, S., Almeida, M., Leontiadis, I., Venieris, S., and Lane, N · 2021
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Federated learning for internet of things: Recent advances, taxonomy, and open challenges
Khan, L. U., Saad, W., Han, Z., Hossain, E., and Hong, C. S · 2021
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Quasi-global momentum: Accelerating decentralized deep learning on heterogeneous data
Lin, T., Karimireddy, S. P., Stich, S. U., and Jaggi, M · 2021
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Federated learning for internet of things: A comprehensive survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., and Poor, H. V · 2021
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Personalized federated learning using hypernetworks
Shamsian, A., Navon, A., Fetaya, E., and Chechik, G · 2021
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Decentralized federated averaging
Sun, T., Li, D., and Wang, B · 2021
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Personalized federated learning by structured and unstructured pruning under data heterogeneity
Vahidian, S., Morafah, M., and Lin, B · 2021
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Swarm learning for decentralized and confidential clinical machine learning
Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N. A., et al · 2021
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Defed: A principled decentralized and privacy-preserving federated learning algorithm
Yuan, Y., Chen, R., Sun, C., Wang, M., Hua, F., Yi, X., Yang, T., and Liu, J · 2021
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Efficient split-mix federated learning for on-demand and in-situ customization
Hong, J., Wang, H., Wang, Z., and Zhou, J · 2022
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Learning to collaborate in decentralized learning of personalized models
Li, S., Zhou, T., Tian, X., and Tao, D · 2022
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Fine-tuning global model via data-free knowledge distillation for non-iid federated learning
Zhang, L., Shen, L., Ding, L., Tao, D., and Duan, L.-Y · 2022
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Topology-aware generalization of decentralized sgd
Zhu, T., He, F., Zhang, L., Niu, Z., Song, M., and Tao, D · 2022
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