7 citations · 9 across the 5 of their papers we have counts for
5 papers
Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays
Charikleia Iakovidou, Kibaek Kim
Federated learning (FL) was recently proposed to securely train models with data held over multiple locations (``clients'') under the coordination of a central server. Prolonged tr…
On the Convergence of NEAR-DGD for Nonconvex Optimization with Second Order Guarantees
Charikleia Iakovidou, Ermin Wei
We consider the setting where the nodes of an undirected, connected network collaborate to solve a shared objective modeled as the sum of smooth functions. We assume that each summ…
S-NEAR-DGD: A Flexible Distributed Stochastic Gradient Method for Inexact Communication
Charikleia Iakovidou, Ermin Wei
We present and analyze a stochastic distributed method (S-NEAR-DGD) that can tolerate inexact computation and inaccurate information exchange to alleviate the problems of costly gr…
Nested Distributed Gradient Methods with Stochastic Computation Errors
Charikleia Iakovidou, Ermin Wei
In this work, we consider the problem of a network of agents collectively minimizing a sum of convex functions. The agents in our setting can only access their local objective func…
Nested Distributed Gradient Methods with Adaptive Quantized Communication
Albert S. Berahas, Charikleia Iakovidou, Ermin Wei
In this paper, we consider minimizing a sum of local convex objective functions in a distributed setting, where communication can be costly. We propose and analyze a class of neste…