98 citations · 291 across the 38 of their papers we have counts for
4 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…
Non-Bayesian Social Learning with Multiview Observations
Dongyan Sui, Weichen Cao, Stefan Vlaski +2
Non-Bayesian social learning enables multiple agents to conduct networked signal and information processing through observing environmental signals and information aggregating. Tra…
Differential error feedback for communication-efficient decentralized learning
Roula Nassif, Stefan Vlaski, Marco Carpentiero +2
Communication-constrained algorithms for decentralized learning and optimization rely on local updates coupled with the exchange of compressed signals. In this context, differentia…
Learned Finite-Time Consensus for Distributed Optimization
Aaron Fainman, Stefan Vlaski
Most algorithms for decentralized learning employ a consensus or diffusion mechanism to drive agents to a common solution of a global optimization problem. Generally this takes the…