7 citations · 12 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Tracking Performance of Online Stochastic Learners
Stefan Vlaski, Elsa Rizk, Ali H. Sayed
The utilization of online stochastic algorithms is popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and proces…
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…