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Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence.
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Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2018, April). When edge meets learning: Adaptive control for resource-constrained distributed machine learning. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications (pp. 63-71). IEEE
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Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan H. B., Van Overveldt, T., Petrou, D., Ramage, D. & Roselander J. (2019). Towards federated learning at scale: System design. In Proceedings of the Conference on Systems and Machine Learning (SysML’19), Palo Alto, CA, USA
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Zheng, S., Meng, Q., Wang, T., Chen, W., Yu, N., Ma, Z. M., & Liu, T. Y. (2017, August). Asynchronous stochastic gradient descent with delay compensation. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 4120-4129)
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Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing. Proceedings of the IEEE, vol. 107, no. 8, pp. 1738-1762, Aug. 2019
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