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The emerging paradigm of federated learning strives to enable collaborative training of machine learning models on the network edge without centrally aggregating raw data and hence, improving data privacy.
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
ud din, M. A., Ivannikova, E., Khan, S. A., Oyomno, W., Fu, Q., Tan, K. E., and Flanagan, A. (2019) · 1901
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FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
Chen, Y., Wang, J., Yu, C., Gao, W., and Qin, X. (2019) · 1907
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Federated Learning: Challenges, Methods, and Future Directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. L. (2019) · 1908
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Distributed Personalization
Miao, X., Chu, C.-T., Tang, L., Zhou, Y., Young, J., and Bhasin, A. (2015) · 1998
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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The MovieLens Datasets: History and Context
Harper, F. M. and Konstan, J. A. (2015) · 2015
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Federated Optimization: Distributed Optimization Beyond the Datacenter
Konecný, J., McMahan, H. B., and Ramage, D. (2015) · 2015
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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) · 2017
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Personalized Image Aesthetics
Ren, J., Shen, X., Lin, Z., Mech, R., and Foran, D. J. (2017) · 2017
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An Overview of Multi-Task Learning in Deep Neural Networks
Ruder, S. (2017) · 2017
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Federated Multi-task Learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A. (2017) · 2017
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Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E., and Nocedal, J. (2018) · 2018
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PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation
Garcia del Molino, A. and Gygli, M. (2018) · 2018
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Federated Learning: Strategies for Improving Communication Efficiency
Konecný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D. (2018) · 2018
Later among the works it cites.
Federated Optimization in Heterogeneous Networks
Sahu, A. K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., and Smith, V. (2018) · 2018
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Split learning for health: Distributed deep learning without sharing raw patient data
Vepakomma, P., Gupta, O., Swedish, T., and Raskar, R. (2018) · 2018
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Chen, F., Dong, Z., Li, Z., and He, X. (2018) · 2018
Cited alongside, same era.
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V. (2018) · 2018
Later among the works it cites.
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y. (2019) · 2019
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