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McMahan, H. Brendan
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1

Learning to Generate Image Embeddings with User-Level Diffe..:

, In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
Xu, Zheng ; Collins, Maxwell ; Wang, Yuxiao... - p. 7969-7980 , 2023
Link: https://doi.org/10.1109/..
 
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2

Practical Secure Aggregation for Privacy-Preserving Machine..:

, In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security,
Bonawitz, Keith ; Ivanov, Vladimir ; Kreuter, Ben... - p. 1175-1191 , 2017
Link: https://dl.acm.org/doi/1..
 
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3

Deep Learning with Differential Privacy:

, In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security,
Abadi, Martin ; Chu, Andy ; Goodfellow, Ian... - p. 308-318 , 2016
Link: https://dl.acm.org/doi/1..
 
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4

Ad click prediction : a view from the trenches:

, In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining,
McMahan, H. Brendan ; Holt, Gary ; Sculley, D.... - p. 1222-1230 , 2013
Link: https://dl.acm.org/doi/1..
 
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5

Efficiently computing minimax expected-size confidence regi..:

, In: Proceedings of the 24th international conference on Machine learning,
Bryan, Brent ; McMahan, H. Brendan ; Schafer, Chad M.. - p. 97-104 , 2007
Link: https://dl.acm.org/doi/1..
 
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6

Online convex optimization in the bandit setting : gradi..:

, In: Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms,
Flaxman, Abraham D. ; Kalai, Adam Tauman ; McMahan, H. Brendan - p. 385-394 , 2005
Link: https://dl.acm.org/doi/1..
 
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7

Bounded real-time dynamic programming : RTDP with monoto..:

, In: Proceedings of the 22nd international conference on Machine learning,
McMahan, H. Brendan ; Likhachev, Maxim ; Gordon, Geoffrey J. - p. 569-576 , 2005
Link: https://dl.acm.org/doi/1..
 
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8

Multi-source spanning trees: algorithms for minimizing sour..:

McMahan, H.Brendan ; Proskurowski, Andrzej
Discrete Applied Mathematics.  137 (2004)  2 - p. 213-222 , 2004
Link: https://doi.org/10.1016/..
 
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9

Advances and open problems in federated learning:

Kairouz, P. (Peter) ; McMahan, H. B. (H. Brendan) ; Avent, B. (Brendan)... , 2021
Link: http://urn.fi/urn:nbn:fi..
 
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10

Unleashing the Power of Randomization in Auditing Different..:

Pillutla, Krishna ; Andrew, Galen ; Kairouz, Peter...
http://arxiv.org/abs/2305.18447.  , 2023
Link: http://arxiv.org/abs/230..
 
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11

(Amplified) Banded Matrix Factorization: A unified approach..:

Choquette-Choo, Christopher A ; Ganesh, Arun ; McKenna, Ryan...
http://arxiv.org/abs/2306.08153.  , 2023
Link: http://arxiv.org/abs/230..
 
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12

Can Public Large Language Models Help Private Cross-device ..:

Wang, Boxin ; Zhang, Yibo Jacky ; Cao, Yuan...
http://arxiv.org/abs/2305.12132.  , 2023
Link: http://arxiv.org/abs/230..
 
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13

An Empirical Evaluation of Federated Contextual Bandit Algo..:

Agarwal, Alekh ; McMahan, H. Brendan ; Xu, Zheng
http://arxiv.org/abs/2303.10218.  , 2023
Link: http://arxiv.org/abs/230..
 
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14

One-shot Empirical Privacy Estimation for Federated Learnin:

Andrew, Galen ; Kairouz, Peter ; Oh, Sewoong...
http://arxiv.org/abs/2302.03098.  , 2023
Link: http://arxiv.org/abs/230..
 
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15

Federated Learning of Gboard Language Models with Different..:

Xu, Zheng ; Zhang, Yanxiang ; Andrew, Galen...
http://arxiv.org/abs/2305.18465.  , 2023
Link: http://arxiv.org/abs/230..
 
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