7 citations · 12 across the 10 of their papers we have counts for
14 papers
Diffusion Learning with Partial Agent Participation and Local Updates
Elsa Rizk, Kun Yuan, Ali H. Sayed
Diffusion learning is a framework that endows edge devices with advanced intelligence. By processing and analyzing data locally and allowing each agent to communicate with its imme…
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates
Elsa Rizk, Kun Yuan, Ali H. Sayed
In this work, we examine a network of agents operating asynchronously, aiming to discover an ideal global model that suits individual local datasets. Our assumption is that each ag…
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…
Local Graph-homomorphic Processing for Privatized Distributed Systems
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
We study the generation of dependent random numbers in a distributed fashion in order to enable privatized distributed learning by networked agents. We propose a method that we ref…