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Papaya: Practical, Private, and Scalable Federated Learning

D. Huba, J. Nguyen, K. Malik, R. Zhu, M. Rabbat, A. Yousefpour, C. Wu, H. Zhan, P. Ustinov, H. Srinivas, K. Wang, A. Shoumikhin, J. Min, and M. Malek. (2021)cite arxiv:2111.04877.

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Sanna Rabbat

Bernd Miketta

Anna Mikecz

Helmut Mikelskis

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Papaya: Practical, Private, and Scalable Federated LearningD. Huba, J. Nguyen, K. Malik, R. Zhu, M. Rabbat, A. Yousefpour, C. Wu, H. Zhan, P. Ustinov, H. Srinivas and 4 other author(s). (2021)cite arxiv:2111.04877.Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning.M. Assran, J. Romoff, N. Ballas, J. Pineau, and M. Rabbat. NeurIPS, page 13299-13309. (2019)Learning with Gradient Descent and Weakly Convex Losses.D. Richards, and M. Rabbat. AISTATS, volume 130 of Proceedings of Machine Learning Research, page 1990-1998. PMLR, (2021)Sustainable AI: Environmental Implications, Challenges and Opportunities.C. Wu, R. Raghavendra, U. Gupta, B. Acun, N. Ardalani, K. Maeng, G. Chang, F. Behram, J. Huang, C. Bai and 15 other author(s). MLSys, mlsys.org, (2022)Masked Siamese Networks for Label-Efficient Learning.M. Assran, M. Caron, I. Misra, P. Bojanowski, F. Bordes, P. Vincent, A. Joulin, M. Rabbat, and N. Ballas. ECCV (31), volume 13691 of Lecture Notes in Computer Science, page 456-473. Springer, (2022)Privacy-aware compression for federated data analysis.K. Chaudhuri, C. Guo, and M. Rabbat. UAI, volume 180 of Proceedings of Machine Learning Research, page 296-306. PMLR, (2022)TarMAC: Targeted Multi-Agent Communication.A. Das, T. Gervet, J. Romoff, D. Batra, D. Parikh, M. Rabbat, and J. Pineau. ICML, volume 97 of Proceedings of Machine Learning Research, page 1538-1546. PMLR, (2019)On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings.M. Assran, and M. Rabbat. ICML, volume 119 of Proceedings of Machine Learning Research, page 410-420. PMLR, (2020)Federated Learning with Buffered Asynchronous Aggregation.J. Nguyen, K. Malik, H. Zhan, A. Yousefpour, M. Rabbat, M. Malek, and D. Huba. AISTATS, volume 151 of Proceedings of Machine Learning Research, page 3581-3607. PMLR, (2022)Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery.K. Maeng, S. Bharuka, I. Gao, M. Jeffrey, V. Saraph, B. Su, C. Trippel, J. Yang, M. Rabbat, B. Lucia and 1 other author(s). MLSys, mlsys.org, (2021)

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Papaya: Practical, Private, and Scalable Federated Learning

Please choose a person to relate this publication to

Sanna Rabbat

Bernd Miketta

Anna Mikecz

Anna Mikecz

Helmut Mikelskis

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Rabbat, Mike"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Papaya: Practical, Private, and Scalable Federated Learning

Please choose a person to relate this publication to

Sanna Rabbat

Bernd Miketta

Anna Mikecz

Anna Mikecz

Helmut Mikelskis

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
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Chicago
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Papaya: Practical, Private, and Scalable Federated Learning