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The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy.
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Variational recurrent adversarial deep domain adaptation
Purushotham, S., Carvalho, W., Nilanon, T., and Liu, Y · 2017
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Federated multi-task learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A. S · 2017
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Decentralized collaborative learning of personalized models over networks
Vanhaesebrouck, P., Bellet, A., and Tommasi, M · 2017
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Variational federated multi-task learning
Corinzia, L., Beuret, A., and Buhmann, J. M · 2019
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Improving federated learning personalization via model agnostic meta learning
Jiang, Y., Konečnỳ, J., Rush, K., and Kannan, S · 2019
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Pytorch: An imperative style, high-performance deep learning library
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Robust and communication-efficient federated learning from non-iid data
Sattler, F., Wiedemann, S., Müller, K.-R., and Samek, W · 2019
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A survey on image data augmentation for deep learning
Shorten, C. and Khoshgoftaar, T. M · 2019
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Interpolation-prediction networks for irregularly sampled time series
Shukla, S. N. and Marlin, B · 2019
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An investigation into on-device personalization of end-to-end automatic speech recognition models
Sim, K. C., Zadrazil, P., and Beaufays, F · 2019
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Federated evaluation of on-device personalization
Wang, K., Mathews, R., Kiddon, C., Eichner, H., Beaufays, F., and Ramage, D · 2019
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Federated learning: A survey on enabling technologies, protocols, and applications
Aledhari, M., Razzak, R., Parizi, R. M., and Saeed, F · 2020
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Federated learning with quantized global model updates
Amiri, M. M., Gunduz, D., Kulkarni, S. R., and Poor, H. V · 2020
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Adaptive personalized federated learning
Deng, Y., Kamani, M. M., and Mahdavi, M · 2020
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Personalized federated learning: A meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
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An efficient framework for clustered federated learning
Ghosh, A., Chung, J., Yin, D., and Ramchandran, K · 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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Federated learning in mobile edge networks: A comprehensive survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., and Miao, C · 2020
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Ensemble distillation for robust model fusion in federated learning
Lin, T., Kong, L., Stich, S. U., and Jaggi, M · 2020
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Energy-based out-of-distribution detection
Liu, W., Wang, X., Owens, J., and Li, Y · 2020
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A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., and Srivastava, G · 2021
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Personalized federated learning using hypernetworks
Shamsian, A., Navon, A., Fetaya, E., and Chechik, G · 2021
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Identifying coordinated accounts on social media through hidden influence and group behaviours
Sharma, K., Zhang, Y., Ferrara, E., and Liu, Y · 2021
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Data-free knowledge distillation for heterogeneous federated learning
Zhu, Z., Hong, J., and Zhou, J · 2021
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Diverse client selection for federated learning via submodular maximization
Balakrishnan, S., Li, T., Tianyi Zhou, N. H., Smith, V., and Bilmes, J · 2022
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Self-aware personalized federated learning
Chen, H., Ding, J., Tramel, E., Wu, S., Sahu, A. K., Avestimehr, S., and Zhang, T · 2022
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Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečnỳ, J., Kumar, S., and McMahan, H. B · 2020
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Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
Sattler, F., Müller, K.-R., and Samek, W · 2020
Cited alongside, same era.
Communication efficiency in federated learning: Achievements and challenges
Shahid, O., Pouriyeh, S., Parizi, R. M., Sheng, Q. Z., Srivastava, G., and Zhao, L · 2020
Cited alongside, same era.
The communication-aware clustered federated learning problem
Shlezinger, N., Rini, S., and Eldar, Y. C · 2020
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Personalized federated learning with moreau envelopes
T Dinh, C., Tran, N., and Nguyen, J · 2020
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Federated learning with matched averaging
Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y · 2020
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Personalized federated learning with gaussian processes
Achituve, I., Shamsian, A., Navon, A., Chechik, G., and Fetaya, E · 2021
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Edispfl: Towards communication-efficient personalized federated learning via decentralized sparse training
Dai, R., Shen, L., He, F., Tian, X., and Tao, D · 2022
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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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Fedchain: Chained algorithms for near-optimal communication cost in federated learning
Hou, C., Thekumparampil, K. K., Fanti, G., and Oh, S · 2022
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Fedpara: Low-rank hadamard product for communication-efficient federated learning
Hyeon-Woo, N., Ye-Bin, M., and Oh, T.-H · 2022
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Factorized-fl: Personalized federated learning with parameter factorization and similarity matching
Jeong, W. and Hwang, S. J · 2022
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Fedpop: A bayesian approach for personalised federated learning
Kotelevskii, N. Y., Vono, M., Durmus, A., and Moulines, E · 2022
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Orchestra: Unsupervised federated learning via globally consistent clustering
Lubana, E. S., Tang, C. I., Kawsar, F., Dick, R., and Mathur, A · 2022
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Towards personalized federated learning
Tan, A. Z., Yu, H., Cui, L., and Yang, Q · 2022
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Open-set recognition: A good closed-set classifier is all you need
Vaze, S., Han, K., Vedaldi, A., and Zisserman, A · 2022
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Personalized federated learning via heterogeneous modular networks
Wang, T., Cheng, W., Luo, D., Yu, W., Ni, J., Tong, L., Chen, H., and Zhang, X · 2022
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Acceleration of federated learning with alleviated forgetting in local training
Xu, C., Hong, Z., Huang, M., and Jiang, T · 2022
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Federated learning with matched averaging
Yu, Y., Wei, A., Karimireddy, S. P., and Yi Ma, M. I. J · 2022
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Divergence-aware federated self-supervised learning
Zhuang, W., Wen, Y., and Zhang, S · 2022
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