98 citations · 291 across the 38 of their papers we have counts for
12 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…
Gramian-Based Adaptive Combination Policies for Diffusion Learning over Networks
Y. Efe Erginbas, Stefan Vlaski, Ali H. Sayed
This paper presents an adaptive combination strategy for distributed learning over diffusion networks. Since learning relies on the collaborative processing of the stochastic infor…
Graph-Homomorphic Perturbations for Private Decentralized Learning
Stefan Vlaski, Ali H. Sayed
Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information as a result of repeated local exchanges of intermediate estimates. Such struc…
Dif-MAML: Decentralized Multi-Agent Meta-Learning
Mert Kayaalp, Stefan Vlaski, Ali H. Sayed
The objective of meta-learning is to exploit the knowledge obtained from observed tasks to improve adaptation to unseen tasks. As such, meta-learners are able to generalize better…