32 citations · 60 across the 6 of their papers we have counts for
6 papers
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…
Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar
Christian A. Schroth, Christian Eckrich, Ibrahim Kakouche +4
The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-a…
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…
Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis
Christian A. Schroth, Michael Muma
This article proposes a new class of Real Elliptically Skewed (RESK) distributions and associated clustering algorithms that allow for integrating robustness and skewness into a si…
Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions
Christian A. Schroth, Michael Muma
Robustly determining the optimal number of clusters in a data set is an essential factor in a wide range of applications. Cluster enumeration becomes challenging when the true unde…