98 citations · 291 across the 38 of their papers we have counts for
5 papers · 1 filter
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…
Exact Subspace Diffusion for Decentralized Multitask Learning
Shreya Wadehra, Roula Nassif, Stefan Vlaski
Classical paradigms for distributed learning, such as federated or decentralized gradient descent, employ consensus mechanisms to enforce homogeneity among agents. While these stra…
Multi-Agent Adversarial Training Using Diffusion Learning
Ying Cao, Elsa Rizk, Stefan Vlaski +1
This work focuses on adversarial learning over graphs. We propose a general adversarial training framework for multi-agent systems using diffusion learning. We analyze the converge…
Decentralized Adversarial Training over Graphs
Ying Cao, Elsa Rizk, Stefan Vlaski +1
The vulnerability of machine learning models to adversarial attacks has been attracting considerable attention in recent years. Most existing studies focus on the behavior of stand…
Enforcing Privacy in Distributed Learning with Performance Guarantees
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the pop…