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Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from different clients.
Advances and open problems in federated learning
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Advances and open problems in federated learning
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Hogwild: A lock-free approach to parallelizing stochastic gradient descent
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Randomized smoothing for stochastic optimization
Duchi, J. C., Bartlett, P. L., and Wainwright, M. J · 2012
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A unified convergence analysis of block successive minimization methods for nonsmooth optimization
Razaviyayn, M., Hong, M., and Luo, Z.-Q · 2013
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A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion
Xu, Y. and Yin, W · 2013
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
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1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns
Seide, F., Fu, H., Droppo, J., Li, G., and Yu, D · 2014
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Scalable distributed DNN training using commodity gpu cloud computing
Strom, N · 2015
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3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J · 2015
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Block stochastic gradient iteration for convex and nonconvex optimization
Xu, Y. and Yin, W · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Asynchronous parallel algorithms for nonconvex big-data optimization: Model and convergence
Cannelli, L., Facchinei, F., Kungurtsev, V., and Scutari, G · 2016
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Learning privately from multiparty data
Hamm, J., Cao, Y., and Belkin, M · 2016
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MIMIC-III, a freely accessible critical care database
Johnson, A. E., Pollard, T. J., Shen, L., Li-wei, H. L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G · 2016
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Arock: an algorithmic framework for asynchronous parallel coordinate updates
Peng, Z., Xu, Y., Yan, M., and Yin, W · 2016
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Sparse communication for distributed gradient descent
Aji, A. F. and Heafield, K · 2017
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QSGD: Communication-efficient SGD via gradient quantization and encoding
Alistarh, D., Grubic, D., Li, J., Tomioka, R., and Vojnovic, M · 2017
Slow and stale gradients can win the race: Error-runtime trade-offs in distributed SGD
Dutta, S., Joshi, G., Ghosh, S., Dube, P., and Nagpurkar, P · 2018
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Cooperative SGD: A unified framework for the design and analysis of communication-efficient SGD algorithms
Wang, J. and Joshi, G · 2018
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Gaussian differential privacy
Dong, J., Roth, A., and Su, W. J · 2019
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Fdml: A collaborative machine learning framework for distributed features
Hu, Y., Niu, D., Yang, J., and Zhou, S · 2019
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A communication efficient vertical federated learning framework
Liu, Y., Kang, Y., Zhang, X., Li, L., Cheng, Y., Chen, T., Hong, M., and Yang, Q · 2019
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Agnostic federated learning
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Cited alongside, same era.
Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
Cited alongside, same era.
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Hardy, S., Henecka, W., Ivey-Law, H., Nock, R., Patrini, G., Smith, G., and Thorne, B · 2017
Cited alongside, same era.
Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent
Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., and Liu, J · 2017
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Random gradient-free minimization of convex functions
Nesterov, Y. and Spokoiny, V · 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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Asynchronous coordinate descent under more realistic assumptions
Sun, T., Hannah, R., and Yin, W · 2017
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Mohri, M., Sivek, G., and Suresh, A. T · 2019
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Secure federated submodel learning
Niu, C., Wu, F., Tang, S., Hua, L., Jia, R., Lv, C., Wu, Z., and Chen, G · 2019
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A comparative analysis of speech signal processing algorithms for parkinson’s disease classification and the use of the tunable q-factor wavelet transform
Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., Tutuncu, M., Aydin, T., Isenkul, M. E., and Apaydin, H · 2019
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A privacy-preserving infrastructure for analyzing personal health data in a vertically partitioned scenario
Sun, C., Ippel, L., van Soest, J., Wouters, B., Malic, A., Adekunle, O., van den Berg, B., Mussmann, O., Koster, A., van der Kallen, C., et al · 2019
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Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
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LASG: Lazily aggregated stochastic gradients for communication-efficient distributed learning
Chen, T., Sun, Y., and Yin, W · 2020
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Think locally, act globally: Federated learning with local and global representations
Liang, P. P., Liu, T., Ziyin, L., Salakhutdinov, R., and Morency, L.-P · 2020
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Multitask learning and benchmarking with clinical time series data
Harutyunyan, H., Khachatrian, H., Kale, D. C., Ver Steeg, G., and Galstyan, A · 2052
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