32 citations · 32 across the 4 of their papers we have counts for
3 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…
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…