Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks

no code implementations • 10 Jan 2024 • Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu

The deployment of federated learning (FL) within vertical heterogeneous networks, such as those enabled by high-altitude platform station (HAPS), offers the opportunity to engage a wide array of clients, each endowed with distinct communication and computational capabilities.

Federated Learning

Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services

no code implementations • 9 May 2023 • Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu

In the ever-expanding landscape of the IoT, managing the intricate network of interconnected devices presents a fundamental challenge.

Federated Learning Management

FLSTRA: Federated Learning in Stratosphere

no code implementations • 1 Feb 2023 • Amin Farajzadeh, Animesh Yadav, Omid Abbasi, Wael Jaafar, Halim Yanikomeroglu

We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data.

Federated Learning