22 citations · 36 across the 24 of their papers we have counts for
7 papers · 1 filter
Sensitivity Curve Maximization: Attacking Robust Aggregators in Distributed Learning
Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir
In distributed learning agents aim at collaboratively solving a global learning problem. It becomes more and more likely that individual agents are malicious or faulty with an incr…
Fast and Robust Sparsity-Aware Block Diagonal Representation
Aylin Tastan, Michael Muma, Abdelhak M. Zoubir
The block diagonal structure of an affinity matrix is a commonly desired property in cluster analysis because it represents clusters of feature vectors by non-zero coefficients tha…
Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization
Wenzhong Yan, Feng Yin, Juntao Wang +3
In this paper, we design Graph Neural Networks (GNNs) with attention mechanisms to tackle an important yet challenging nonlinear regression problem: massive network localization. W…
Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers
Zhi-Yong Wang, Hing Cheung So, Abdelhak M. Zoubir
To alleviate the bias generated by the l1-norm in the low-rank tensor completion problem, nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear norm,…
Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization
Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir
Distributed learning paradigms, such as federated or decentralized learning, allow a collection of agents to solve global learning and optimization problems through limited local i…
Robust and Efficient Aggregation for Distributed Learning
Stefan Vlaski, Christian Schroth, Michael Muma +1
Distributed learning paradigms, such as federated and decentralized learning, allow for the coordination of models across a collection of agents, and without the need to exchange r…