806 citations · 7.4k across the 169 of their papers we have counts for
19 papers · 1 filter
Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks
Zhaoxian Wu, Qing Ling, Tianyi Chen +1
This paper deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, most resilient appro…
Learning Connectivity and Higher-order Interactions in Radial Distribution Grids
Qiuling Yang, Mario Coutino, Gang Wang +2
To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has…
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation
Jun Sun, Gang Wang, Georgios B. Giannakis +2
Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investig…
A Statistical Learning Approach to Reactive Power Control in Distribution Systems
Qiuling Yang, Alireza Sadeghi, Gang Wang +2
Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, moti…
Edge Dithering for Robust Adaptive Graph Convolutional Networks
Vassilis N. Ioannidis, Georgios B. Giannakis
Graph convolutional networks (GCNs) are vulnerable to perturbations of the graph structure that are either random, or, adversarially designed. The perturbed links modify the graph…
GraphSAC: Detecting anomalies in large-scale graphs
Vassilis N. Ioannidis, Dimitris Berberidis, Georgios B. Giannakis
A graph-based sampling and consensus (GraphSAC) approach is introduced to effectively detect anomalous nodes in large-scale graphs. Existing approaches rely on connectivity and att…