Built on

K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecný, S. Mazzocchi, H. B. McMahan, T. V. Overveldt, D. Petrou, D. Ramage, and J. Roselander, “Towards federated learning at scale: System design,” CoRR , vol. abs/1902.01046, 2019. [Online]. Available: http://arxiv.org/abs/1902.01046
Original
1902
Earlier work this paper cites.
P. J. Bickel, E. A. Hammel, and J. W. O’Connell, “Sex bias in graduate admissions: Data from Berkeley,” Science , vol. 187, no. 4175, pp. 398–404, 1975
1975
Earlier work this paper cites.
Y. Nesterov, “A method for solving the convex programming problem with convergence rate o ⁡ ( 1 / k 2 ) o(1/{k}^{2}) ,” Dokl. Akad. Nauk SSSR , vol. 269, pp. 543–547, 1983. [Online]. Available: https://ci.nii.ac.jp/naid/10029946121/en/
1983
Earlier work this paper cites.
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492 , 2016
Original
2016
Earlier work this paper cites.
J. Konečnỳ, H. B. McMahan, D. Ramage, and P. Richtárik, “Federated optimization: Distributed machine learning for on-device intelligence,” arXiv preprint arXiv:1610.02527 , 2016
Original
2016
Earlier work this paper cites.
M. Hardt, E. Price, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds., vol. 29. Curran Associates, Inc., 2016. [Online]. Available: https://proceedings.neurips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial Intelligence and Statistics . PMLR, 2017, pp. 1273–1282
2017
Earlier work this paper cites.
V. Smith, C.-K. Chiang, M. Sanjabi, and A. Talwalkar, “Federated multi-task learning,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , 2017, pp. 4427–4437
2017
Earlier work this paper cites.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 1175–1191
2017
Earlier work this paper cites.
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A search space odyssey,” IEEE transactions on neural networks and learning systems , vol. 28, no. 10, pp. 2222–2232, 2017
2017
Earlier work this paper cites.
N. Agarwal, A. T. Suresh, F. X. Yu, S. Kumar, and B. McMahan, “cpSGD: Communication-efficient and differentially-private distributed SGD,” in Proceedings of NeurIPS , 2018, pp. 7575–7586
2018
Earlier work this paper cites.
B. E. Woodworth, J. Wang, A. D. Smith, B. McMahan, and N. Srebro, “Graph oracle models, lower bounds, and gaps for parallel stochastic optimization,” in Proceedings of NeurIPS , 2018, pp. 8505–8515
2018
Earlier work this paper cites.

Similar

T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” arXiv preprint arXiv:1812.06127 , 2018
Original
2018
Cited alongside, same era.
A. Hard, K. Rao, R. Mathews, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, “Federated learning for mobile keyboard prediction,” arXiv preprint arXiv:1811.03604 , 2018
Original
2018
Cited alongside, same era.
S. Caldas, S. M. K. Duddu, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar, “Leaf: A benchmark for federated settings,” arXiv preprint arXiv:1812.01097 , 2018
Original
2018
Cited alongside, same era.
M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” in International Conference on Machine Learning , 2019, pp. 4615–4625
2019
Cited alongside, same era.
Y. Jiang, J. Konečnỳ, K. Rush, and S. Kannan, “Improving federated learning personalization via model agnostic meta learning,” arXiv preprint arXiv:1909.12488 , 2019

Then

V. Kulkarni, M. Kulkarni, and A. Pant, “Survey of personalization techniques for federated learning,” in 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) . IEEE, 2020, pp. 794–797
2020
Later among the works it cites.
A. Agarwal, J. Langford, and C.-Y. Wei, “Federated residual learning,” arXiv preprint arXiv:2003.12880 , 2020
Original
2020
Later among the works it cites.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine , vol. 37, no. 3, pp. 50–60, 2020
2020
Later among the works it cites.
A. Abay, Y. Zhou, N. Baracaldo, S. Rajamoni, E. Chuba, and H. Ludwig, “Mitigating bias in federated learning,” 2020
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Reading the bibliography…

2021

Communication-Efficient Agnostic Federated Averaging

Ro, Jae, Chen, Mingqing, Mathews, Rajiv et al.

Understand

In distributed learning settings such as federated learning, the training algorithm can be potentially biased towards different clients.

Mohri et al.
(2019) proposed a domain-agnostic learning algorithm, where the model is optimized for any target distribution formed by a mixture of the client distributions in order to overcome this bias.
They further proposed an algorithm for the cross-silo federated learning setting, where the number of clients is small.

Built on

K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecný, S. Mazzocchi, H. B. McMahan, T. V. Overveldt, D. Petrou, D. Ramage, and J. Roselander, “Towards federated learning at scale: System design,” CoRR , vol. abs/1902.01046, 2019. [Online]. Available: http://arxiv.org/abs/1902.01046
Original
1902
Earlier work this paper cites.
P. J. Bickel, E. A. Hammel, and J. W. O’Connell, “Sex bias in graduate admissions: Data from Berkeley,” Science , vol. 187, no. 4175, pp. 398–404, 1975
1975
Earlier work this paper cites.
Y. Nesterov, “A method for solving the convex programming problem with convergence rate o ⁡ ( 1 / k 2 ) o(1/{k}^{2}) ,” Dokl. Akad. Nauk SSSR , vol. 269, pp. 543–547, 1983. [Online]. Available: https://ci.nii.ac.jp/naid/10029946121/en/
1983
Earlier work this paper cites.
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492 , 2016
Original
2016
Earlier work this paper cites.
J. Konečnỳ, H. B. McMahan, D. Ramage, and P. Richtárik, “Federated optimization: Distributed machine learning for on-device intelligence,” arXiv preprint arXiv:1610.02527 , 2016
Original
2016
Earlier work this paper cites.
M. Hardt, E. Price, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds., vol. 29. Curran Associates, Inc., 2016. [Online]. Available: https://proceedings.neurips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial Intelligence and Statistics . PMLR, 2017, pp. 1273–1282
2017
Earlier work this paper cites.
V. Smith, C.-K. Chiang, M. Sanjabi, and A. Talwalkar, “Federated multi-task learning,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , 2017, pp. 4427–4437
2017
Earlier work this paper cites.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 1175–1191
2017
Earlier work this paper cites.
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A search space odyssey,” IEEE transactions on neural networks and learning systems , vol. 28, no. 10, pp. 2222–2232, 2017
2017
Earlier work this paper cites.
N. Agarwal, A. T. Suresh, F. X. Yu, S. Kumar, and B. McMahan, “cpSGD: Communication-efficient and differentially-private distributed SGD,” in Proceedings of NeurIPS , 2018, pp. 7575–7586
2018
Earlier work this paper cites.
B. E. Woodworth, J. Wang, A. D. Smith, B. McMahan, and N. Srebro, “Graph oracle models, lower bounds, and gaps for parallel stochastic optimization,” in Proceedings of NeurIPS , 2018, pp. 8505–8515
2018
Earlier work this paper cites.

Similar

T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” arXiv preprint arXiv:1812.06127 , 2018
Original
2018
Cited alongside, same era.
A. Hard, K. Rao, R. Mathews, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, “Federated learning for mobile keyboard prediction,” arXiv preprint arXiv:1811.03604 , 2018
Original
2018
Cited alongside, same era.
S. Caldas, S. M. K. Duddu, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar, “Leaf: A benchmark for federated settings,” arXiv preprint arXiv:1812.01097 , 2018
Original
2018
Cited alongside, same era.
M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” in International Conference on Machine Learning , 2019, pp. 4615–4625
2019
Cited alongside, same era.
Y. Jiang, J. Konečnỳ, K. Rush, and S. Kannan, “Improving federated learning personalization via model agnostic meta learning,” arXiv preprint arXiv:1909.12488 , 2019

Then

V. Kulkarni, M. Kulkarni, and A. Pant, “Survey of personalization techniques for federated learning,” in 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) . IEEE, 2020, pp. 794–797
2020
Later among the works it cites.
A. Agarwal, J. Langford, and C.-Y. Wei, “Federated residual learning,” arXiv preprint arXiv:2003.12880 , 2020
Original
2020
Later among the works it cites.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine , vol. 37, no. 3, pp. 50–60, 2020
2020
Later among the works it cites.
A. Abay, Y. Zhou, N. Baracaldo, S. Rajamoni, E. Chuba, and H. Ludwig, “Mitigating bias in federated learning,” 2020
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2019

Cited alongside, same era.

J. Hamer, M. Mohri, and A. T. Suresh, “Fedboost: A communication-efficient algorithm for federated learning,” in International Conference on Machine Learning . PMLR, 2020, pp. 3973–3983

2020

Cited alongside, same era.

D. Basu, D. Data, C. Karakus, and S. N. Diggavi, “Qsparse-local-sgd: Distributed sgd with quantization, sparsification, and local computations,” IEEE Journal on Selected Areas in Information Theory , vol. 1, no. 1, pp. 217–226, 2020

2020

Cited alongside, same era.

S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learning,” in International Conference on Machine Learning . PMLR, 2020, pp. 5132–5143

2020

Cited alongside, same era.

J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor, “Tackling the objective inconsistency problem in heterogeneous federated optimization,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 7611–7623. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/564127c03caab942e503ee6f810f54fd-Paper.pdf

2020

Cited alongside, same era.

S. P. Karimireddy, M. Jaggi, S. Kale, M. Mohri, S. J. Reddi, S. U. Stich, and A. T. Suresh, “Mime: Mimicking centralized stochastic algorithms in federated learning,” arXiv preprint arXiv:2008.03606 , 2020

Original

2020

Cited alongside, same era.

Z. Li and P. Richtárik, “A unified analysis of stochastic gradient methods for nonconvex federated optimization,” arXiv preprint arXiv:2006.07013 , 2020

Original

2020

Cited alongside, same era.

Y. Mansour, M. Mohri, J. Ro, and A. T. Suresh, “Three approaches for personalization with applications to federated learning,” arXiv preprint arXiv:2002.10619 , 2020

Original

2020

Cited alongside, same era.

T. Li, M. Sanjabi, A. Beirami, and V. Smith, “Fair resource allocation in federated learning,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=ByexElSYDr

2020

Later among the works it cites.

J. H. Ro, A. T. Suresh, and K. Wu, “FedJAX: Federated learning simulation with JAX,” 2020. [Online]. Available: http://github.com/google/fedjax

2020

Later among the works it cites.

S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečný, S. Kumar, and H. B. McMahan, “Adaptive federated optimization,” 2020

2020

Later among the works it cites.

F. Haddadpour, M. M. Kamani, A. Mokhtari, and M. Mahdavi, “Federated learning with compression: Unified analysis and sharp guarantees,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2021, pp. 2350–2358

2021

Closest in time.

P. Kairouz, Z. Liu, and T. Steinke, “The distributed discrete gaussian mechanism for federated learning with secure aggregation,” arXiv preprint arXiv:2102.06387 , 2021

Original

2021

Closest in time.

D. A. E. Acar, Y. Zhao, R. M. Navarro, M. Mattina, P. N. Whatmough, and V. Saligrama, “Federated learning based on dynamic regularization,” in International Conference on Learning Representations , 2021

2021

Closest in time.

Y. Mansour, M. Mohri, J. Ro, A. T. Suresh, and K. Wu, “A theory of multiple-source adaptation with limited target labeled data,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2021, pp. 2332–2340

2021

Closest in time.

H. B. McMahan et al. , “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning , vol. 14, no. 1, 2021

2021

Closest in time.