2023 Journal Issues September 16, 2023
Regular Papers
By J. K. Eshraghian, M. Ward, E. O. Neftci, X. Wang, G. Lenz, G. Dwivedi, M. Bennamoun, D. S. Jeong, and W. D. Lu
This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience to biologically plausible spiking neural networks.
By Y. Li, C. Yu, M. Shahidehpour, T. Yang, Z. Zeng, and T. Chai
This article provides a detailed and well-organized overview of deep reinforcement learning (DRL) methodologies, which encompasses fundamental concepts and theoretical DRL principles, as well as the most sophisticated DRL techniques applied to power system operations.
By C. Ma, J. Li, K. Wei, B. Liu, M. Ding, L. Yuan, Z. Han, and H. V. Poor
This article provides an exhaustive overview of attacks and defensive mechanisms on privacy and security for distributed learning on four different levels, namely sharing data, sharing model, sharing knowledge, and sharing results.