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Federated Learning (FL) is a novel distributed machine learning which allows thousands of edge devices to train model locally without uploading data concentrically to the server.
D.-M. Chiu and R. Jain, “Analysis of the increase and decrease algorithms for congestion avoidance in computer networks,” Computer Networks and ISDN systems , vol. 17, no. 1, pp. 1–14, 1989
1989
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
M. Allman, V. Paxson, W. Stevens et al. , “Tcp congestion control,” 1999
1999
Earlier work this paper cites.
B. Settles, “Active learning literature survey,” 2009
2009
Earlier work this paper cites.
A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N project report, Stanford , vol. 1, no. 12, p. 2009, 2009
2009
Earlier work this paper cites.
J. He, H.-H. Chen, T. M. Chen, and W. Cheng, “Adaptive congestion control for dsrc vehicle networks,” IEEE communications letters , vol. 14, no. 2, pp. 127–129, 2010
2010
Earlier work this paper cites.
D. Haynes, S. Corns, and G. K. Venayagamoorthy, “An exponential moving average algorithm,” in 2012 IEEE Congress on Evolutionary Computation . IEEE, 2012, pp. 1–8
2012
Earlier work this paper cites.
O. Shamir, N. Srebro, and T. Zhang, “Communication-efficient distributed optimization using an approximate newton-type method,” in International conference on machine learning , 2014, pp. 1000–1008
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
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. S. Talwalkar, “Federated multi-task learning,” in Advances in Neural Information Processing Systems , 2017, pp. 4424–4434
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 , 2017, pp. 1175–1191
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
G. Cohen, S. Afshar, J. Tapson, and A. Van Schaik, “Emnist: Extending mnist to handwritten letters,” in 2017 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2017, pp. 2921–2926
2017
Cited alongside, same era.
2018
Cited alongside, same era.
T. S. Brisimi, R. Chen, T. Mela, A. Olshevsky, I. C. Paschalidis, and W. Shi, “Federated learning of predictive models from federated electronic health records,” International journal of medical informatics , vol. 112, pp. 59–67, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
M. Duan, D. Liu, X. Chen, Y. Tan, J. Ren, L. Qiao, and L. Liang, “Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications,” in 2019 IEEE 37th International Conference on Computer Design (ICCD) . IEEE, 2019, pp. 246–254
2019
Later among the works it cites.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smithy, “Feddane: A federated newton-type method,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers . IEEE, 2019, pp. 1227–1231
2019
Later among the works it cites.
X. Yao, T. Huang, C. Wu, R. Zhang, and L. Sun, “Towards faster and better federated learning: A feature fusion approach,” in 2019 IEEE International Conference on Image Processing (ICIP) . IEEE, 2019, pp. 175–179
2019
Later among the works it cites.
H. Yu, S. Yang, and S. Zhu, “Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 5693–5700
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2018
Cited alongside, same era.
2018
Cited alongside, same era.
H. Tang, S. Gan, C. Zhang, T. Zhang, and J. Liu, “Communication compression for decentralized training,” Advances in Neural Information Processing Systems , vol. 31, pp. 7652–7662, 2018
2018
Cited alongside, same era.
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Federated learning for ultra-reliable low-latency v2v communications,” in 2018 IEEE Global Communications Conference (GLOBECOM) . IEEE, 2018, pp. 1–7
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 10, no. 2, pp. 1–19, 2019
2019
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” in 36th International Conference on Machine Learning, ICML 2019 . International Machine Learning Society (IMLS), 2019, pp. 8114–8124
2019
Later among the works it cites.
T. Li, M. Sanjabi, A. Beirami, and V. Smith, “Fair resource allocation in federated learning,” in International Conference on Learning Representations , 2019
2019
Later among the works it cites.
A. Reisizadeh, H. Taheri, A. Mokhtari, H. Hassani, and R. Pedarsani, “Robust and communication-efficient collaborative learning,” in Advances in Neural Information Processing Systems , 2019, pp. 8388–8399
2019
Later among the works it cites.
2019
Later among the works it cites.
L. Yang, B. Tan, V. W. Zheng, K. Chen, and Q. Yang, “Federated recommendation systems,” in Federated Learning . Springer, 2020, pp. 225–239
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.
S. A. Rahman, H. Tout, A. Mourad, and C. Talhi, “Fedmccs: Multi criteria client selection model for optimal iot federated learning,” IEEE Internet of Things Journal , 2020
2020
Later among the works it cites.