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Cross-device Federated Learning (FL) is a distributed learning paradigm with several challenges that differentiate it from traditional distributed learning, variability in the system characteristics on each device, and millions of clients coordinating with a central server being primary ones.
Handbook of methods of applied statistics. volume i: Techniques of computation descriptive methods, and statistical inference. volume ii: Planning of surveys and experiments, 1967
Chakravarti, I., Laha, R., and Roy, J · 1967
Earlier work this paper cites.
Secure communications over insecure channels
Merkle, R. C · 1978
Earlier work this paper cites.
Probabilistic encryption & how to play mental poker keeping secret all partial information
Goldwasser, S. and Micali, S · 1982
Earlier work this paper cites.
A robust and verifiable cryptographically secure election scheme
Cohen, J. D. and Fischer, M. J · 1985
Earlier work this paper cites.
A public key cryptosystem and a signature scheme based on discrete logarithms
ElGamal, T · 1985
Earlier work this paper cites.
Parallel and Distributed Computation: Numerical Methods
Bertsekas, D. P. and Tsitsiklis, J. N · 1989
Earlier work this paper cites.
Analyzing scalability of parallel algorithms and architectures
Kumar, V. and Gupta, A · 1994
Earlier work this paper cites.
Public-key cryptosystems based on composite degree residuosity classes
Paillier, P · 1999
Earlier work this paper cites.
Character-aware neural language models
Kim, Y., Jernite, Y., Sontag, D. A., and Rush, A. M · 2015
Earlier work this paper cites.
Practical secure aggregation for federated learning on user-held data
Bonawitz, K. A., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2016
Cited alongside, same era.
Federated learning of deep networks using model averaging
McMahan, H. B., Moore, E., Ramage, D., and Agüera y Arcas, B · 2016
Cited alongside, same era.
On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P · 2017
Cited alongside, same era.
Leaf: A benchmark for federated settings
Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečnỳ, J., McMahan, H. B., Smith, V., and Talwalkar, A · 2018
Cited alongside, same era.
Secure single-server aggregation with (poly) logarithmic overhead
Bell, J. H., Bonawitz, K. A., Gascón, A., Lepoint, T., and Raykova, M · 2020
Later among the works it cites.
Cryptonite: A framework for flexible time-series secure aggregation with online fault tolerance
Karl, R., Takeshita, J., and Jung, T · 2020
Later among the works it cites.
Ibm federated learning: an enterprise framework white paper v0. 1
Ludwig, H., Baracaldo, N., Thomas, G., Zhou, Y., Anwar, A., Rajamoni, S., Ong, Y., Radhakrishnan, J., Verma, A., Sinn, M., et al · 2020
Later among the works it cites.
Adaptive federated optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečnỳ, J., Kumar, S., and McMahan, H. B · 2020
Later among the works it cites.
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Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečnỳ, J., Mazzocchi, S., McMahan, H. B., et al · 2019
Cited alongside, same era.
Federated learning for mobile keyboard prediction, 2019
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Machine learning at facebook: Understanding inference at the edge
Wu, C.-J., Brooks, D., Chen, K., Chen, D., Choudhury, S., Dukhan, M., Hazelwood, K., Isaac, E., Jia, Y., Jia, B., Leyvand, T., Lu, H., Lu, Y., Qiao, L., Reagen, B., Spisak, J., Sun, F., Tulloch, A., Vajda, P., Wang, X., Wang, Y., Wasti, B., Wu, Y., Xian, R., Yoo, S., and Zhang, P · 2019
Cited alongside, same era.
Asynchronous federated optimization
Xie, C., Koyejo, S., and Gupta, I · 2019
Cited alongside, same era.
URL https://github.com/google/trillian
Trillian: General transparency
Cited in the paper.
URL https://transparency.dev/verifiable-data-structures/
Verifiable data structures
Cited in the paper.
Ibm federated learning
IBM
Cited in the paper.
Charles, Z., Garrett, Z., Huo, Z., Shmulyian, S., and Smith, V · 2021
Closest in time.
Federated learning with buffered asynchronous aggregation
Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., Rabbat, M., Esmaeili, M. M., and Huba, D · 2021
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Federated evaluation and tuning for on-device personalization: System design & applications
Paulik, M., Seigel, M., Mason, H., Telaar, D., Kluivers, J., van Dalen, R. C., Lau, C. W., Carlson, L., Granqvist, F., Vandevelde, C., Agarwal, S., Freudiger, J., Byde, A., Bhowmick, A., Kapoor, G., Beaumont, S., Cahill, Á., Hughes, D., Javidbakht, O., Dong, F., Rishi, R., and Hung, S · 2021
Closest in time.
OpenFL: An open-source framework for federated learning, 2021
Reina, G. A., Gruzdev, A., Foley, P., Perepelkina, O., Sharma, M., Davidyuk, I., Trushkin, I., Radionov, M., Mokrov, A., Agapov, D., Martin, J., Edwards, B., Sheller, M. J., Pati, S., Moorthy, P. N., Wang, S., Shah, P., and Bakas, S · 2021
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Asynchronous federated learning on heterogeneous devices: A survey
Xu, C., Qu, Y., Xiang, Y., and Gao, L · 2021
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