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In this paper we introduce "Federated Learning Utilities and Tools for Experimentation" (FLUTE), a high-performance open-source platform for federated learning research and offline simulations.
On the convergence of fedavg on non-iid data
Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z · 1907
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Federated Learning: Challenges, Methods, and Future Directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 1908
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Practical federated gradient boosting decision trees
Li, Q., Wen, Z., and He, B · 1911
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The perceptron: A probabilistic model for information storage and organization in the brain
Rosenblatt, F · 1958
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Federated Transfer Learning with Dynamic Gradient Aggregation
Dimitriadis, D., Kumatani, K., Gmyr, R., Gaur, Y., and Eskimez, E. S · 2008
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Twitter sentiment classification using distant supervision
Go, A., Bhayani, R., and Huang, L · 2009
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Bandwidth Optimal All-reduce Algorithms for Clusters of Workstations
Patarasuk, P. and Yuan, X · 2009
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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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Distributed delayed stochastic optimization
Agarwal, A. and Duchi, J. C · 2011
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Communication Efficient Distributed Optimization Using an Approximate Newton-type Method
Shamir, O., Srebro, N., and Zhang, T · 2013
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Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars)
Diao, Q., Qiu, M., Wu, C.-Y., Smola, A. J., Jiang, J., and Wang, C · 2014
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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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Federated Optimization: Distributed Optimization Beyond the Datacenter
Konecny, J., McMahan, B. H., and Ramage, D · 2015
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Asynchronous parallel stochastic gradient for nonconvex optimization
Lian, X., Huang, Y., Li, Y., and Liu, J · 2015
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LibriSpeech: an ASR corpus based on public domain audio books
Panayotov, V., Chen, G., Povey, D., and Khudanpur, S · 2015
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Scalable distributed dnn training using commodity gpu cloud computing
Strom, N · 2015
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Document modeling with gated recurrent neural network for sentiment classification
Tang, D., Qin, B., and Liu, T · 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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AzureML: Anatomy of a Machine Learning service
AzureML Team · 2016
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Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering
Chen, K. and Huo, Q · 2016
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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
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Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A · 2017
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2017
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Communication-efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and Arcas, B · 2017
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Rényi differential privacy
Mironov, I · 2017
An overview of federated deep learning privacy attacks and defensive strategies
Enthoven, D. and Al-Ars, Z · 2020
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Generating representative headlines for news stories
Gu, X., Mao, Y., Han, J., Liu, J., Wu, Y., Yu, C., Finnie, D., Yu, H., Zhai, J., and Zukoski, N · 2020
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Fedml: A research library and benchmark for federated machine learning
He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., Zhu, X., Wang, J., Shen, L., Zhao, P., Kang, Y., Liu, Y., Raskar, R., Yang, Q., Annavaram, M., and Avestimehr, S · 2020
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SCAFFOLD: Stochastic controlled averaging for federated learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S., and Suresh, A. T · 2020
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Cited alongside, same era.
Large batch training of convolutional networks
You, Y., Gitman, I., and Ginsburg, B · 2017
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Slow and stale gradients can win the race: Error-runtime trade-offs in distributed sgd
Dutta, S., Joshi, G., Ghosh, S., P., D., and P., N · 2018
Cited alongside, same era.
Horovod: Fast and Easy Distributed Deep Learning in TensorFlow
Sergeev, A. and Bals, M. D · 2018
Cited alongside, same era.
Gradient sparsification for communication-efficient distributed optimization
Wangni, J., Wang, J., Liu, J., and Zhang, T · 2018
Cited alongside, same era.
Federated learning with non-iid data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V · 2018
Cited alongside, same era.
Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis
Ben-Nun, T. and Hoefler, T · 2019
Cited alongside, same era.
Liang, X., Javid, A. M., Skoglund, M., and Chatterjee, S · 2020
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Adaptive federated optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečnỳ, J., Kumar, S., and McMahan, H. B · 2020
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Federated learning with differential privacy: Algorithms and performance analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., and Poor, H. V · 2020
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Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
You, Y., Li, J., Reddi, S., Hseu, J., Kumar, S., Bhojanapalli, S., Song, X., Demmel, J., Keutzer, K., and Hsieh, C.-J · 2020
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Dynamic Gradient Aggregation for Federated Domain Adaptation
Dimitriadis, D., Kumatani, K., Gmyr, R., Gaur, Y., and Eskimez, S. E · 2021
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Numerical composition of differential privacy
Gopi, S., Lee, Y. T., and Wutschitz, L · 2021
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Fedgraphnn: A federated learning system and benchmark for graph neural networks
He, C., Balasubramanian, K., Ceyani, E., Yang, C., Xie, H., Sun, L., He, L., Yang, L., Yu, P. S., Rong, Y., Zhao, P., Huang, J., Annavaram, M., and Avestimehr, S · 2021
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Adaptive quantization of model updates for communication-efficient federated learning
Jhunjhunwala, D., Gadhikar, A., Joshi, G., and Eldar, Y. C · 2021
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Adaptive federated optimization
Reddi, S. J., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., and McMahan, H. B · 2021
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A Guide to GDPR Data Privacy Requirements
Wolford, B · 2021
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PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems
Zhang, J., Chen, B., Cheng, X., Binh, H. T. T., and Yu, S · 2021
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Optimal accounting of differential privacy via characteristic function
Zhu, Y., Dong, J., and Wang, Y · 2021
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Heterogeneous ensemble knowledge transfer for training large models in federated learning
Cho, Y. J., Manoel, A., Joshi, G., Sim, R., and Dimitriadis, D · 2022
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Federated learning with a sampling algorithm under isoperimetry, 2022
Sun, L., Salim, A., and Richtárik, P · 2022
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Invariant aggregator for defending federated backdoor attacks, 2022
Wang, X., Dimitriadis, D., Koyejo, S., and Tople, S · 2022
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