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We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research.
AI benchmark: All about deep learning on smartphones in 2019
Ignatov, A., Timofte, R., Kulik, A., Yang, S., Wang, K., Baum, F., Wu, M., Xu, L., and Gool, L. V · 1910
Earlier work this paper cites.
Europarl: A Parallel Corpus for Statistical Machine Translation
Koehn, P · 2005
Earlier work this paper cites.
Effects of age and gender on blogging
Schler, J., Koppel, M., Argamon, S., and Pennebaker, J · 2006
Earlier work this paper cites.
Image-based recommendations on styles and substitutes
McAuley, J., Targett, C., Shi, Q., and van den Hengel, A · 2015
Earlier work this paper cites.
Hollywood in homes: Crowdsourcing data collection for activity understanding
Sigurdsson, G. A., Varol, G., Wang, X., Farhadi, A., Laptev, I., and Gupta, A · 2016
Earlier work this paper cites.
EMNIST: an extension of MNIST to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A · 2017
Earlier work this paper cites.
Differentially private federated learning: A client level perspective
Geyer, R. C., Klein, T., and Nabi, M · 2017
Earlier work this paper cites.
Neural adaptive video streaming with Pensieve
Mao, H., Netravali, R., and Alizadeh, M · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Earlier work this paper cites.
From lifestyle vlogs to everyday interactions
Fouhey, D. F., Kuo, W., Efros, A. A., and Malik, J · 2018
Earlier work this paper cites.
Amc: Automl for model compression and acceleration on mobile devices
He, Y., Lin, J., Liu, Z., Wang, H., Li, L.-J., and Han, S · 2018
Earlier work this paper cites.
To relay or not to relay for inter-cloud transfers?
Lai, F., Chowdhury, M., and Madhyastha, H · 2018
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A. G., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Earlier work this paper cites.
Speech commands: A dataset for limited-vocabulary speech recognition
Warden, P · 2018
Earlier work this paper cites.
Applied federated learning: Improving Google keyboard query suggestions
Yang, T., Andrew, G., Eichner, H., Sun, H., Li, W., Kong, N., Ramage, D., and Beaufays, F · 2018
Earlier work this paper cites.
https://sites.google.com/view/fgvc6/competitions/inaturalist-2019
iNaturalist 2019 · 2019
Cited alongside, same era.
Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., and et al · 2019
Cited alongside, same era.
Leaf: A benchmark for federated settings
Caldas, S., Meher, S., Duddu, K., and et al · 2019
Cited alongside, same era.
Semi-cyclic stochastic gradient descent
Eichner, H., Koren, T., McMahan, H. B., Srebro, N., and Talwar, K · 2019
Cited alongside, same era.
Error feedback fixes signsgd and other gradient compression schemes
Karimireddy, S. P., Rebjock, Q., Stich, S. U., and Jaggi, M · 2019
Cited alongside, same era.
Mlperf training benchmark
Mattson, P., Cheng, C., Coleman, C., and et al · 2020
Later among the works it cites.
Adaptive federated optimization
Reddi, S., Charles, Z., and et al · 2020
Later among the works it cites.
Fetchsgd: Communication-efficient federated learning with sketching
Rothchild, D., Panda, A., Ullah, E., Ivkin, N., Stoica, I., Braverman, V., Gonzalez, J., and Arora, R · 2020
Later among the works it cites.
Attack of the tails: Yes, you really can backdoor federated learning
Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., yong Sohn, J., Lee, K., and Papailiopoulos, D · 2020
Later among the works it cites.
Google landmarks dataset v2 a large-scale benchmark for instance-level recognition and retrieval
Weyand, T., Araujo, A., Cao, B., and Sim, J · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Reddy, S., Chen, D., and Manning, C. D · 2019
Cited alongside, same era.
Can you really backdoor federated learning?
Sun, Z., Kairouz, P., Suresh, A. T., and McMahan, H. B · 2019
Cited alongside, same era.
Libritts: A corpus derived from librispeech for text-to-speech
Zen, H., Dang, V., Clark, R., Zhang, Y., Weiss, R. J., Jia, Y., Chen, Z., and Wu, Y · 2019
Cited alongside, same era.
FedEval: A benchmark system with a comprehensive evaluation model for federated learning
Chai, D., Wang, L., Chen, K., and Yang, Q · 2020
Cited alongside, same era.
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
Cited alongside, same era.
Inverting gradients - how easy is it to break privacy in federated learning?
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M · 2020
Cited alongside, same era.
FedML: A research library and benchmark for federated machine learning
He, C., Li, S., So, J., and Zeng, X · 2020
Cited alongside, same era.
Learning in situ: a randomized experiment in video streaming
Yan, F. Y., Ayers, H., and et al · 2020
Later among the works it cites.
Fine-grained gpu sharing primitives for deep learning applications
Yu, P. and Chowdhury, M · 2020
Later among the works it cites.
Flower: A friendly federated learning framework
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., de Gusmao, P. P. B., and Lane, N. D · 2021
Closest in time.
Large scale interactive motion forecasting for autonomous driving : The waymo open motion dataset
Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., Chai, Y., Sapp, B., Qi, C., Zhou, Y., Yang, Z., Chouard, A., Sun, P., Ngiam, J., Vasudevan, V., McCauley, A., Shlens, J., and Anguelov, D · 2021
Closest in time.
Oort: Efficient federated learning via guided participant selection
Lai, F., Zhu, X., Madhyastha, H. V., and Chowdhury, M · 2021
Closest in time.
FedJAX: Federated learning simulation with jax
Ro, J. H., Suresh, A. T., and Wu, K · 2021
Closest in time.
Fed-ensemble: Improving generalization through model ensembling in federated learning
Shi, N., Lai, F., Kontar, R. A., and Chowdhury, M · 2021
Closest in time.
Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data
Yang, C., Wang, Q., and et al · 2021
Closest in time.
PyramidFL: Fine-grained data and system heterogeneity-aware client selection for efficient federated learning
Li, C., Zeng, X., Zhang, M., and Cao, Z · 2022
Closest in time.
Swan: A neural engine for efficient dnn training on smartphone socs
Singapuram, S. S. V., Lai, F., Hu, C., and Chowdhury, M · 2022
Closest in time.