10 citations · 12 across the 27 of their papers we have counts for
5 papers · 1 filter
FedHV: Low-Overhead Hypervolume Weighting for Federated Multi-Objective Optimization
Amirardalan Dehghanpour, Seyed Mohammad Azimi-Abarghouyi, Christopher G. Brinton
Task-wise federated multi-objective optimization (FedMOO) trains a shared model for competing prediction objectives under heterogeneous data, partial participation, and communicati…
Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design
Seyed Mohammad Azimi-Abarghouyi, Mehdi Bennis, Leandros Tassiulas
Federated learning (FL) is fundamentally a distributed optimization problem executed by communicating agents with local data, local computation, and partial system visibility. Once…
A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees
Seyed Mohammad Azimi-Abarghouyi, Carlo Fischione
Almost all existing hierarchical federated learning (FL) models are limited to two aggregation layers, restricting scalability and flexibility in complex, large-scale networks. In…
Hierarchical Federated ADMM
Seyed Mohammad Azimi-Abarghouyi, Nicola Bastianello, Karl H. Johansson +1
In this paper, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alter…
A Hierarchical Federated Learning Approach for the Internet of Things
Seyed Mohammad Azimi-Abarghouyi, Viktoria Fodor
This paper presents a novel federated learning solution, QHetFed, suitable for large-scale Internet of Things deployments, addressing the challenges of large geographic span, commu…