Byzantine-Robust and Efficient Federated Learning for the Internet of Things

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Jin, Rui, et al. “Byzantine-Robust and Efficient Federated Learning for the Internet of Things”. IEEE Internet of Things Magazine, vol. 5, no. 1, 2022, pp. 114-8, https://doi.org/10.1109/iotm.001.2100192.
Jin, R., Hu, J., Min, G., & Lin, H. (2022). Byzantine-Robust and Efficient Federated Learning for the Internet of Things. IEEE Internet of Things Magazine, 5(1), 114-118. https://doi.org/10.1109/iotm.001.2100192
Jin R, Hu J, Min G, Lin H. Byzantine-Robust and Efficient Federated Learning for the Internet of Things. IEEE Internet of Things Magazine. 2022;5(1):114-8.
Refrences
Title Journal Journal Categories Citations Publication Date
Learning from History for Byzantine Robust Optimization 2021
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent 2017
Communication-Efficient Learning of Deep Networks from Decentralized Data 2017
Federated Learning for Mobile Keyboard Prediction 2018
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates