Fetching the paper…
Reading the bibliography…
Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models.
Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H.; Qi, H.; and Brown, M. 2019 · 1909
Earlier work this paper cites.
Advances and open problems in federated learning
Kairouz, P.; McMahan, H. B.; Avent, B.; Bellet, A.; Bennis, M.; Bhagoji, A. N.; Bonawitz, K.; Charles, Z.; Cormode, G.; Cummings, R.; et al. 2019 · 1912
Earlier work this paper cites.
Object recognition with gradient-based learning
LeCun, Y.; Haffner, P.; Bottou, L.; and Bengio, Y. 1999 · 1999
Earlier work this paper cites.
Inverting Gradients–How easy is it to break privacy in federated learning?
Geiping, J.; Bauermeister, H.; Dröge, H.; and Moeller, M. 2020 · 2003
Earlier work this paper cites.
Quality Inference in Federated Learning with Secure Aggregation
Pejó, B.; and Biczók, G. 2020 · 2007
Earlier work this paper cites.
Communication-efficient federated learning via optimal client sampling
Ribero, M.; and Vikalo, H. 2020 · 2007
Earlier work this paper cites.
The smallest singular value of random combinatorial matrices
Tran, T. 2020 · 2007
Earlier work this paper cites.
Secure aggregation with heterogeneous quantization in federated learning
Elkordy, A. R.; and Avestimehr, A. S. 2020 · 2009
Earlier work this paper cites.
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning
Kadhe, S.; Rajaraman, N.; Koyluoglu, O. O.; and Ramchandran, K. 2020 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A.; and Hinton, G. 2009 · 2009
Earlier work this paper cites.
Optimal Client Sampling for Federated Learning
Chen, W.; Horvath, S.; and Richtarik, P. 2020 · 2010
Earlier work this paper cites.
Cho, Y. J.; Wang, J.; and Joshi, G. 2020 · 2010
Earlier work this paper cites.
Singularity of discrete random matrices II
Jain, V.; Sah, A.; and Sawhney, M. 2020 · 2010
Earlier work this paper cites.
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning
Cho, Y. J.; Gupta, S.; Joshi, G.; and Yağan, O. 2020 · 2012
Earlier work this paper cites.
Communication-Computation Efficient Secure Aggregation for Federated Learning
Choi, B.; Sohn, J.-y.; Han, D.-J.; and Moon, J. 2020 · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Dwork, C.; Roth, A.; et al. 2014 · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
Cited alongside, same era.
Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M.; Jha, S.; and Ristenpart, T. 2015 · 2015
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M.; Chu, A.; Goodfellow, I.; McMahan, H. B.; Mironov, I.; Talwar, K.; and Zhang, L. 2016 · 2016
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 · 2017
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for federated learning
Karimireddy, S. P.; Kale, S.; Mohri, M.; Reddi, S.; Stich, S.; and Suresh, A. T. 2020 · 2020
Later among the works it cites.
Big transfer (bit): General visual representation learning
Kolesnikov, A.; Beyer, L.; Zhai, X.; Puigcerver, J.; Yung, J.; Gelly, S.; and Houlsby, N. 2020 · 2020
Later among the works it cites.
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.; and Poor, H. V. 2020 · 2020
Later among the works it cites.
Deep leakage from gradients
Zhu, L.; and Han, S. 2020 · 2020
Later among the works it cites.
Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data
Bonawitz, K.; Kairouz, P.; McMahan, B.; and Ramage, D. 2021 · 2021
Closest in time.
Advances and open problems in federated learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
McMahan, H. B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
Cited alongside, same era.
Learning differentially private recurrent language models
McMahan, H. B.; Ramage, D.; Talwar, K.; and Zhang, L. 2018 · 2018
Cited alongside, same era.
Local SGD converges fast and communicates little
Stich, S. U. 2018 · 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 · 2018
Cited alongside, same era.
On the Convergence of FedAvg on Non-IID Data
Li, X.; Huang, K.; Yang, W.; Wang, S.; and Zhang, Z. 2019 · 2019
Cited alongside, same era.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Nasr, M.; Shokri, R.; and Houmansadr, A. 2019 · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M.; and Le, Q. 2019 · 2019
Cited alongside, same era.
Kairouz, P.; McMahan, H. B.; Avent, B.; Bellet, A.; Bennis, M.; Bhagoji, A. N.; Bonawitz, K.; Charles, Z.; Cormode, G.; Cummings, R.; et al. 2021 · 2021
Closest in time.
MNIST handwritten digit database
LeCun, Y.; Cortes, C.; and Burges, C. 2010 · 2021
Closest in time.
Secure aggregation for buffered asynchronous federated learning
So, J.; Ali, R. E.; Güler, B.; and Avestimehr, A. S. 2021 · 2021
Closest in time.
Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
So, J.; Güler, B.; and Avestimehr, A. S. 2021 · 2021
Closest in time.
FedGP: Correlation-Based Active Client Selection for Heterogeneous Federated Learning
Tang, M.; Ning, X.; Wang, Y.; Wang, Y.; and Chen, Y. 2021 · 2021
Closest in time.
LightSecAgg: Rethinking Secure Aggregation in Federated Learning
Yang, C.-S.; So, J.; He, C.; Li, S.; Yu, Q.; and Avestimehr, S. 2021 · 2021
Closest in time.
Information Theoretic Secure Aggregation with User Dropouts
Zhao, Y.; and Sun, H. 2021 · 2021
Closest in time.
On multi-round Privacy in Federated Learning
Deer, A.; Ali, R. E.; and Avestimehr, A. S. 2022 · 2022
Closest in time.
Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning
So, J.; He, C.; Yang, C.-S.; Li, S.; Yu, Q.; E Ali, R.; Guler, B.; and Avestimehr, S. 2022 · 2022
Closest in time.