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Federated Learning is a fast growing area of ML where the training datasets are extremely distributed, all while dynamically changing over time.
Catastrophic forgetting in connectionist networks
French, R. M · 1999
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Crafting papers on machine learning
Langley, P · 2000
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Federated Optimization: Distributed Optimization Beyond the Datacenter
Konecny, J., McMahan, B. H., and Ramage, D · 2015
Earlier work this paper cites.
Inverting face embeddings with convolutional neural networks
Zhmoginov, A. and Sandler, M · 2016
Earlier work this paper cites.
Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A · 2017
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Synthesizing normalized faces from facial identity features
Cole, F., Belanger, D., Krishnan, D., Sarna, A., Mosseri, I., and Freeman, W. T · 2017
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Variational deep embedding: An unsupervised and generative approach to clustering
Jiang, Z., Zheng, Y., Tan, H., Tang, B., and Zhou, H · 2017
Cited alongside, same era.
Communication-efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and Arcas, B. A. y · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Rebuffi, S. A., Kolesnikov, A., Sperl, G., and Lampert, C. H · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J., and Kim, J · 2017
Cited alongside, same era.
Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Hsu, Y.-C., Liu, Y.-C., Ramasamy, A., and Kira, Z · 2018
Cited alongside, same era.
Continual learning with deep generative replay
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., and He, B · 2020
Later among the works it cites.
Federated continual learning with adaptive parameter communication
Yoon, J., Jeong, W., Lee, G., Yang, E., and Hwang, S. J · 2020
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Concept drift detection and adaptation for federated and continual learning
Casado, F., Lema, D., Criado, M., Iglesias Rodriguez, R., Regueiro, C., and Barro, S · 2021
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A continual learning survey: Defying forgetting in classification tasks
Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T · 2021
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Towards causal federated learning for enhanced robustness and privacy
Francis, S., Tenison, I., and Rish, I · 2021
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Javed, K. and White, M · 2019
Cited alongside, same era.
An introduction to variational autoencoders
Kingma, D. P. and Welling, M · 2019
Cited alongside, same era.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2020
Cited alongside, same era.
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Advances and open problems in federated learning
Kairouz, P. and al · 2021
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Pseudo-rehearsal for continual learning with normalizing flows
Pomponi, J., Scardapane, S., and Uncini, A · 2021
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A field guide to federated optimization
Wang, J. and al · 2021
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