4 citations · 4 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
A Graph Federated Architecture with Privacy Preserving Learning
Elsa Rizk, Ali H. Sayed
Federated learning involves a central processor that works with multiple agents to find a global model. The process consists of repeatedly exchanging estimates, which results in th…
Federated Learning under Importance Sampling
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
Federated learning encapsulates distributed learning strategies that are managed by a central unit. Since it relies on using a selected number of agents at each iteration, and sinc…
Second-Order Guarantees in Federated Learning
Stefan Vlaski, Elsa Rizk, Ali H. Sayed
Federated learning is a useful framework for centralized learning from distributed data under practical considerations of heterogeneity, asynchrony, and privacy. Federated architec…
Optimal Importance Sampling for Federated Learning
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
Federated learning involves a mixture of centralized and decentralized processing tasks, where a server regularly selects a sample of the agents and these in turn sample their loca…
Dynamic Federated Learning
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in…