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In real-world federated learning scenarios, participants could have their own personalized labels which are incompatible with those from other clients, due to using different label permutations or tackling completely different tasks or domains.
Speeding-up convolutional neural networks using fine-tuned cp-decomposition
Lebedev, V., Ganin, Y., Rakhuba, M., Oseledets, I., and Lempitsky, V · 2014
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Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
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Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2018
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Federated learning with personalization layers, 2019
Arivazhagan, M. G., Aggarwal, V., Singh, A. K., and Choudhary, S · 2019
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Agnostic federated learning, 2019
Mohri, M., Sivek, G., and Suresh, A. T · 2019
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Clustered federated learning: Model-agnostic distributed multi-task optimization under privacy constraints, 2019
Sattler, F., Müller, K.-R., and Samek, W · 2019
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Adaptive personalized federated learning, 2020
Deng, Y., Kamani, M. M., and Mahdavi, M · 2020
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Personalized federated learning: A meta-learning approach, 2020
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
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Federated optimization in heterogeneous networks, 2020
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2020
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Think locally, act globally: Federated learning with local and global representations, 2020
Liang, P. P., Liu, T., Ziyin, L., Allen, N. B., Auerbach, R. P., Brent, D., Salakhutdinov, R., and Morency, L.-P · 2020
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Three approaches for personalization with applications to federated learning, 2020
Mansour, Y., Mohri, M., Ro, J., and Suresh, A. T · 2020
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Stable low-rank tensor decomposition for compression of convolutional neural network, 2020
Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavsky, P., Glukhov, V., Oseledets, I., and Cichocki, A · 2020
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Federated knowledge distillation, 2020
Seo, H., Park, J., Oh, S., Bennis, M., and Kim, S.-L · 2020
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Federated semi-supervised learning with inter-client consistency & disjoint learning
Jeong, W., Yoon, J., Yang, E., and Hwang, S. J · 2021
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Scaffold: Stochastic controlled averaging for federated learning, 2021
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., and Suresh, A. T · 2021
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Model-contrastive federated learning, 2021
Li, Q., He, B., and Song, D · 2021
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Personalized federated learning using hypernetworks
Shamsian, A., Navon, A., Fetaya, E., and Chechik, G · 2021
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Federated continual learning with weighted inter-client transfer, 2021
Yoon, J., Jeong, W., Lee, G., Yang, E., and Hwang, S. J · 2021
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Personalized federated learning with first order model optimization
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Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y · 2020
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Heterofl: Computation and communication efficient federated learning for heterogeneous clients, 2021
Diao, E., Ding, J., and Tarokh, V · 2021
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Fedgroup: Efficient clustered federated learning via decomposed data-driven measure, 2021
Duan, M., Liu, D., Ji, X., Liu, R., Liang, L., Chen, X., and Tan, Y · 2021
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Federated learning of a mixture of global and local models, 2021
Hanzely, F. and Richtárik, P · 2021
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Zhang, M., Sapra, K., Fidler, S., Yeung, S., and Alvarez, J. M · 2021
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Data-free knowledge distillation for heterogeneous federated learning, 2021
Zhu, Z., Hong, J., and Zhou, J · 2021
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Fedpara: Low-rank hadamard product for communication-efficient federated learning
Nam, H.-W., Ye-Bin, M., and Oh, T.-H · 2022
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