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Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized.
Fair resource allocation in federated learning
T. Li, M. Sanjabi, and V. Smith · 1905
Earlier work this paper cites.
The dining cryptographers problem: Unconditional sender and recipient untraceability
D. Chaum · 1988
Earlier work this paper cites.
Multitask learning
R. Caruana · 1997
Earlier work this paper cites.
Practical byzantine fault tolerance
M. Castro, B. Liskov, et al · 1999
Earlier work this paper cites.
Privacy-preserving data mining
R. Agrawal and R. Srikant · 2000
Earlier work this paper cites.
Privacy preserving data mining
Y. Lindell and B. Pinkas · 2000
Earlier work this paper cites.
Regularized multi–task learning
T. Evgeniou and M. Pontil · 2004
Earlier work this paper cites.
Model-based approximate querying in sensor networks
A. Deshpande, C. Guestrin, S. R. Madden, J. M. Hellerstein, and W. Hong · 2005
Earlier work this paper cites.
Tinydb: an acquisitional query processing system for sensor networks
S. R. Madden, M. J. Franklin, J. M. Hellerstein, and W. Hong · 2005
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Earlier work this paper cites.
Distributed systems: principles and paradigms
A. S. Tanenbaum and M. Van Steen · 2007
Earlier work this paper cites.
On early stopping in gradient descent learning
Y. Yao, L. Rosasco, and A. Caponnetto · 2007
Earlier work this paper cites.
Protecting privacy using k-anonymity
K. El Emam and F. K. Dankar · 2008
Earlier work this paper cites.
Multi-core for mobile phones
C. Van Berkel · 2009
Earlier work this paper cites.
δ \delta -presence without complete world knowledge
M. E. Nergiz and C. Clifton · 2010
Earlier work this paper cites.
A survey on wearable sensor-based systems for health monitoring and prognosis
A. Pantelopoulos and N. G. Bourbakis · 2010
Earlier work this paper cites.
Parallelized stochastic gradient descent
M. Zinkevich, M. Weimer, L. Li, and A. J. Smola · 2010
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2011
Earlier work this paper cites.
Differentially private empirical risk minimization
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate · 2011
Earlier work this paper cites.
A firm foundation for private data analysis
C. Dwork · 2011
Earlier work this paper cites.
Divide-and-conquer matrix factorization
L. W. Mackey, M. I. Jordan, and A. Talwalkar · 2011
Earlier work this paper cites.
Hogwild: A lock-free approach to parallelizing stochastic gradient descent
B. Recht, C. Re, S. Wright, and F. Niu · 2011
Earlier work this paper cites.
Fog computing and its role in the internet of things
F. Bonomi, R. Milito, J. Zhu, and S. Addepalli · 2012
Earlier work this paper cites.
Optimal distributed online prediction using mini-batches
O. Dekel, R. Gilad-Bachrach, O. Shamir, and L. Xiao · 2012
Earlier work this paper cites.
Privacy aware learning
J. C. Duchi, M. I. Jordan, and M. J. Wainwright · 2012
Earlier work this paper cites.
Challenges and solutions of ubiquitous user modeling
T. Kuflik, J. Kay, and B. Kummerfeld · 2012
Earlier work this paper cites.
Learning to learn
S. Thrun and L. Pratt · 2012
Earlier work this paper cites.
A public domain dataset for human activity recognition using smartphones
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz · 2013
Earlier work this paper cites.
Estimation, optimization, and parallelism when data is sparse
J. Duchi, M. I. Jordan, and B. McMahan · 2013
Earlier work this paper cites.
More effective distributed ML via a stale synchronous parallel parameter server
Q. Ho, J. Cipar, H. Cui, S. Lee, J. K. Kim, P. B. Gibbons, G. A. Gibson, G. Ganger, and E. P. Xing · 2013
Earlier work this paper cites.
Mobile fog: A programming model for large-scale applications on the internet of things
K. Hong, D. Lillethun, U. Ramachandran, B. Ottenwälder, and B. Koldehofe · 2013
Earlier work this paper cites.
An in-depth study of lte: effect of network protocol and application behavior on performance
J. Huang, F. Qian, Y. Guo, Y. Zhou, Q. Xu, Z. M. Mao, S. Sen, and O. Spatscheck · 2013
Earlier work this paper cites.
Data center networks: Topologies, architectures and fault-tolerance characteristics
Y. Liu, J. K. Muppala, M. Veeraraghavan, D. Lin, and M. Hamdi · 2013
Earlier work this paper cites.
Privacy-preserving ridge regression on hundreds of millions of records
V. Nikolaenko, U. Weinsberg, S. Ioannidis, M. Joye, D. Boneh, and N. Taft · 2013
Earlier work this paper cites.
Fast convergence of stochastic gradient descent under a strong growth condition
M. Schmidt and N. L. Roux · 2013
Earlier work this paper cites.
Accelerated mini-batch stochastic dual coordinate ascent
S. Shalev-Shwartz and T. Zhang · 2013
Earlier work this paper cites.
Trading computation for communication: Distributed stochastic dual coordinate ascent
T. Yang · 2013
Earlier work this paper cites.
Privacy preserving back-propagation neural network learning made practical with cloud computing
J. Yuan and S. Yu · 2013
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
R. Bassily, A. Smith, and A. Thakurta · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
Earlier work this paper cites.
Communication-efficient distributed dual coordinate ascent
M. Jaggi, V. Smith, M. Takác, J. Terhorst, S. Krishnan, T. Hofmann, and M. I. Jordan · 2014
Earlier work this paper cites.
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns
F. Seide, H. Fu, J. Droppo, G. Li, and D. Yu · 2014
Earlier work this paper cites.
Distributed stochastic optimization and learning
O. Shamir and N. Srebro · 2014
Earlier work this paper cites.
Communication-efficient distributed optimization using an approximate newton-type method
O. Shamir, N. Srebro, and T. Zhang · 2014
Earlier work this paper cites.
Machine learning classification over encrypted data
R. Bost, R. A. Popa, S. Tu, and S. Goldwasser · 2015
Earlier work this paper cites.
High-performance distributed ML at scale through parameter server consistency models
W. Dai, A. Kumar, J. Wei, Q. Ho, G. Gibson, and E. P. Xing · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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Edge-centric computing: Vision and challenges
P. Garcia Lopez, A. Montresor, D. Epema, A. Datta, T. Higashino, A. Iamnitchi, M. Barcellos, P. Felber, and E. Riviere · 2015
Cited alongside, same era.
A comprehensive comparison of multiparty secure additions with differential privacy
S. Goryczka and L. Xiong · 2015
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Distributed box-constrained quadratic optimization for dual linear svm
C.-P. Lee and D. Roth · 2015
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Adding vs. averaging in distributed primal-dual optimization
Federated learning for ultra-reliable low-latency v2v communications
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah · 2018
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S. Silva, B. Gutman, E. Romero, P. M. Thompson, A. Altmann, and M. Lorenzi · 2018
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Cocoa: a general framework for communication-efficient distributed optimization
V. Smith, S. Forte, C. Ma, M. Takac, M. I. Jordan, and M. Jaggi · 2018
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Communication compression for decentralized training
H. Tang, S. Gan, C. Zhang, T. Zhang, and J. Liu · 2018
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Technical privacy metrics: a systematic survey
I. Wagner and D. Eckhoff · 2018
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C. Ma, V. Smith, M. Jaggi, M. I. Jordan, P. Richtárik, and M. Takáč · 2015
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Quartz: Randomized dual coordinate ascent with arbitrary sampling
Z. Qu, P. Richtárik, and T. Zhang · 2015
Cited alongside, same era.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Cited alongside, same era.
Federated learning: strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
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Efficient private statistics with succinct sketches
L. Melis, G. Danezis, and E. D. Cristofaro · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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Aide: Fast and communication efficient distributed optimization
S. J. Reddi, J. Konečnỳ, P. Richtárik, B. Póczós, and A. Smola · 2016
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J. Wang and G. Joshi · 2018
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Federated learning white paper v1.0
WeBank AI Group · 2018
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Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
B. Woodworth, J. Wang, A. Smith, B. McMahan, and N. Srebro · 2018
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Gradient diversity: a key ingredient for scalable distributed learning
D. Yin, A. Pananjady, M. Lam, D. Papailiopoulos, K. Ramchandran, and P. Bartlett · 2018
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Parallel restarted sgd for non-convex optimization with faster convergence and less communication
H. Yu, S. Yang, and S. Zhu · 2018
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Federated learning with non-iid data
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra · 2018
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On the convergence properties of a
F. Zhou and G. Cong · 2018
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Federated collaborative filtering for privacy-preserving personalized recommendation system
M. Ammad-ud din, E. Ivannikova, S. A. Khan, W. Oyomno, Q. Fu, K. E. Tan, and A. Flanagan · 2019
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Towards federated learning at scale: system design
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Secure computation for machine learning with spdz
V. Chen, V. Pastro, and M. Raykova · 2019
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Variational federated multi-task learning
L. Corinzia and J. M. Buhmann · 2019
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Semi-cyclic stochastic gradient descent
H. Eichner, T. Koren, H. B. McMahan, N. Srebro, and K. Talwar · 2019
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Scalable and differentially private distributed aggregation in the shuffled model
B. Ghazi, R. Pagh, and A. Velingker · 2019
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N. Guha, A. Talwalkar, and V. Smith · 2019
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L. Huang and D. Liu · 2019
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Towards practical differentially private convex optimization
R. Iyengar, J. P. Near, D. Song, O. Thakkar, A. Thakurta, and L. Wang · 2019
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Incentive design for efficient federated learning in mobile networks: A contract theory approach
J. Kang, Z. Xiong, D. Niyato, H. Yu, Y.-C. Liang, and D. I. Kim · 2019
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Adaptive gradient-based meta-learning methods
M. Khodak, M.-F. Balcan, and A. Talwalkar · 2019
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Decentralized bayesian learning over graphs
A. Lalitha, X. Wang, O. Kilinc, Y. Lu, T. Javidi, and F. Koushanfar · 2019
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Edge-assisted hierarchical federated learning with non-iid data
L. Liu, J. Zhang, S. Song, and K. B. Letaief · 2019
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Exploiting unintended feature leakage in collaborative learning
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Agnostic federated learning
M. Mohri, G. Sivek, and A. T. Suresh · 2019
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Client selection for federated learning with heterogeneous resources in mobile edge
T. Nishio and R. Yonetani · 2019
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Fault tolerance in iterative-convergent machine learning
A. Qiao, B. Aragam, B. Zhang, and E. Xing · 2019
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Federated learning for emoji prediction in a mobile keyboard
S. Ramaswamy, R. Mathews, K. Rao, and F. Beaufays · 2019
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SysML: The new frontier of machine learning systems
A. Ratner et al · 2019
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Coded computation over heterogeneous clusters
A. Reisizadeh, S. Prakash, R. Pedarsani, and A. S. Avestimehr · 2019
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Robust and communication-efficient federated learning from non-iid data
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Local sgd converges fast and communicates little
S. U. Stich · 2019
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Distributed learning over unreliable networks
H. Tang, C. Yu, C. Renggli, S. Kassing, A. Singla, D. Alistarh, J. Liu, and C. Zhang · 2019
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Differentially private learning with adaptive clipping
O. Thakkar, G. Andrew, and H. B. McMahan · 2019
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Fast and faster convergence of sgd for over-parameterized models (and an accelerated perceptron)
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Reducing leakage in distributed deep learning for sensitive health data
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Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd
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Adaptive federated learning in resource constrained edge computing systems
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Federated machine learning: Concept and applications
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On the linear speedup analysis of communication efficient momentum sgd for distributed non-convex optimization
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Bayesian nonparametric federated learning of neural networks
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