4 citations · 10 across the 18 of their papers we have counts for
14 papers · 1 filter
Deep-Relative-Trust-Based Diffusion for Decentralized Deep Learning
Muyun Li, Aaron Fainman, Stefan Vlaski
Decentralized learning strategies allow a collection of agents to learn efficiently from local data sets without the need for central aggregation or orchestration. Current decentra…
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…
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…