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Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud.
Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C. M., Konečný, J., Mazzocchi, S., McMahan, B., Overveldt, T. V., Petrou, D., Ramage, D., and Roselander, J · 1902
Earlier work this paper cites.
Chowdhery, A., Warden, P., Shlens, J., Howard, A., and Rhodes, R · 1906
Earlier work this paper cites.
Cifar-10 (canadian institute for advanced research)
Krizhevsky, A., Nair, V., and Hinton, G · 2005
Earlier work this paper cites.
Large scale distributed deep networks
Dean, J., Corrado, G. S., Monga, R., Chen, K., Devin, M., Le, Q. V., Mao, M. Z., Ranzato, M., Senior, A., Tucker, P., Yang, K., and Ng, A. Y · 2012
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Earlier work this paper cites.
Communication quantization for data-parallel training of deep neural networks
Dryden, N., Jacobs, S. A., Moon, T., and Van Essen, B · 2016
Earlier work this paper cites.
Redeye: Analog convnet image sensor architecture for continuous mobile vision
LiKamWa, R., Hou, Y., Gao, J., Polansky, M., and Zhong, L · 2016
Earlier work this paper cites.
On-body localization of wearable devices: An investigation of position-aware activity recognition
Sztyler, T. and Stuckenschmidt, H · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Accelerating mobile audio sensing algorithms through on-chip GPU offloading
Georgiev, P., Lane, N. D., Mascolo, C., and Chu, D · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Earlier work this paper cites.
Federated multi-task learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A. S · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Earlier work this paper cites.
Analyzing federated learning through an adversarial lens
Bhagoji, A. N., Chakraborty, S., Mittal, P., and Calo, S. B · 2018
Earlier work this paper cites.
JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečnỳ, J., McMahan, H. B., Smith, V., and Talwalkar, A · 2018
Cited alongside, same era.
A hitchhiker’s guide on distributed training of deep neural networks
Chahal, K. S., Grover, M. S., and Dey, K · 2018
Cited alongside, same era.
Heterogeneous bitwidth binarization in convolutional neural networks
Fromm, J., Patel, S., and Philipose, M · 2018
Cited alongside, same era.
Jia, X., Song, S., He, W., Wang, Y., Rong, H., Zhou, F., Xie, L., Guo, Z., Yang, Y., Yu, L., Chen, T., Hu, G., Shi, S., and Chu, X · 2018
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., and Wolf, T · 2019
Later among the works it cites.
Pytorch lightning, 2019
W. Falcon, e. a · 2019
Later among the works it cites.
Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance
Xie, C., Koyejo, S., and Gupta, I · 2019
Later among the works it cites.
Latent backdoor attacks on deep neural networks
Yao, Y., Li, H., Zheng, H., and Zhao, B. Y · 2019
Later among the works it cites.
Secure single-server aggregation with (poly) logarithmic overhead
Bell, J. H., Bonawitz, K. A., Gascón, A., Lepoint, T., and Raykova, M · 2020
Closest in time.
grpc: A high performance, open-source universal rpc framework
Foundation, C. N. C · 2020
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Cited alongside, same era.
Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2018
Cited alongside, same era.
Ray: A distributed framework for emerging ai applications, 2018
Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W., Jordan, M. I., and Stoica, I · 2018
Cited alongside, same era.
A generic framework for privacy preserving deep learning
Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., and Passerat-Palmbach, J · 2018
Cited alongside, same era.
Horovod: fast and easy distributed deep learning in tensorflow
Sergeev, A. and Balso, M. D · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction, 2019
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2019
Cited alongside, same era.
Occlumency: Privacy-preserving remote deep-learning inference using sgx
Lee, T., Lin, Z., Pushp, S., Li, C., Liu, Y., Lee, Y., Xu, F., Xu, C., Zhang, L., and Song, J · 2019
Cited alongside, same era.
Fair resource allocation in federated learning
Li, T., Sanjabi, M., and Smith, V · 2019
Cited alongside, same era.
Closest in time.
Tensorflow federated: Machine learning on decentralized data
Google · 2020
Closest in time.
Federated optimization in heterogeneous networks, 2020
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2020
Closest in time.
On-device model personalization
Lite, T · 2020
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The state of mobile network experience 2020: One year into the 5g era
OpenSignal · 2020
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Deep convlstm with self-attention for human activity decoding using wearable sensors
Singh, S. P., Sharma, M. K., Lay-Ekuakille, A., Gangwar, D., and Gupta, S · 2020
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Model architecture for android devices
Flower · 2021
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Fedscale: Benchmarking model and system performance of federated learning
Lai, F., Dai, Y., Zhu, X., and Chowdhury, M · 2021
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Adaptive federated optimization, 2021
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., and McMahan, H. B · 2021
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