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In Federated Learning (FL), the clients learn a single global model (FedAvg) through a central aggregator.
Fair resource allocation in federated learning
Li, T., Sanjabi, M., and Smith, V · 1905
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Improving federated learning personalization via model agnostic meta learning
Jiang, Y., Konecný, J., Rush, K., and Kannan, S · 1909
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Federated learning with personalization layers
Arivazhagan, M. G., Aggarwal, V., Singh, A. K., and Choudhary, S · 1912
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Propriétés des applications ‘prox’
Moreau, J.-J · 1963
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A quantitative measure of fairness and discrimination for resource allocation in shared computer systems
Jain, R., Chiu, D., and Hawe, W · 1998
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Think locally, act globally: Federated learning with local and global representations
Liang, P. P., Liu, T., Liu, Z., Salakhutdinov, R., and Morency, L · 2001
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Salvaging federated learning by local adaptation
Yu, T., Bagdasaryan, E., and Shmatikov, V · 2002
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Domain adaptation: Learning bounds and algorithms
Mansour, Y., Mohri, M., and Rostamizadeh, A · 2009
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A survey on transfer learning
Pan, S. J. and Yang, Q · 2009
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Federated learning of deep networks using model averaging
McMahan, H. B., Moore, E., Ramage, D., and y Arcas, B. A · 2016
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Federated multi-task learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A. S · 2017
Cited alongside, same era.
Federated meta-learning for recommendation
Chen, F., Dong, Z., Li, Z., and He, X · 2018
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Federated learning for mobile keyboard prediction
Hard, A., Rao, K., Mathews, R., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2018
Personalized Federated Learning with Moreau Envelopes
Dinh, C. T., Tran, N. H., and Dung Nguyen, T · 2020
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Personalized Federated Learning: A Meta-Learning Approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
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Li, A., Sun, J., Wang, B., Duan, L., Li, S., Chen, Y., and Li, H · 2020
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Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2020
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Three Approaches for Personalization with Applications to Federated Learning
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Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J. and Carbin, M · 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.
Adaptive Personalized Federated Learning
Deng, Y., Mahdi Kamani, M., and Mahdavi, M · 2020
Cited alongside, same era.
Mansour, Y., Mohri, M., Ro, J., and Theertha Suresh, A · 2020
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Federated mutual learning, 2020
Shen, T., Zhang, J., Jia, X., Zhang, F., Huang, G., Zhou, P., Kuang, K., Wu, F., and Wu, C · 2020
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Unifying distillation with personalization in federated learning, 2021
Divi, S., Farrukh, H., and Celik, B · 2021
Closest in time.
Personalized cross-silo federated learning on non-iid data, 2021
Huang, Y., Chu, L., Zhou, Z., Wang, L., Liu, J., Pei, J., and Zhang, Y · 2021
Closest in time.