Inference load-aware orchestration for hierarchical federated learning

A Lackinger, PA Frangoudis, I Čilić… - 2024 IEEE 49th …, 2024 - ieeexplore.ieee.org
Hierarchical federated learning (HFL) designs introduce intermediate aggregator nodes
between clients and the global federated learning server in order to reduce communication …

Self-aware collaborative edge inference with embedded devices for IIoT

Y Chen, Z Yu, Y **, C Mwase, X Hu, L Da Xu… - Future Generation …, 2025 - Elsevier
Edge inference and other compute-intensive industrial Internet of Things (IIoT) applications
suffer from a bad quality of experience due to the limited and heterogeneous computing and …

Condar: Context-aware distributed dynamic object detection on radar data

I Harshbarger, C Zejda… - MILCOM 2024-2024 IEEE …, 2024 - ieeexplore.ieee.org
Deep neural networks are an established class of algorithms widely used for real-time data
analysis in computer vision-based mobile applications. However, on one hand the …

UAV-Assisted Split Computing System: Design and Performance Optimization

H Yeom, J Lee, H Ko - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
In the conventional split computing approach based on the external computing node (eg,
cloud), Internet of Things (IoT) devices suffer from high network latency. In this article, we …

Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication

A Furutanpey, PA Frangoudis, P Szabo… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper investigates the adversarial robustness of Deep Neural Networks (DNNs) using
Information Bottleneck (IB) objectives for task-oriented communication systems. We …

Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning

A Danilenka, A Furutanpey, VC Pujol, B Sedlak… - arxiv preprint arxiv …, 2024 - arxiv.org
Handling heterogeneity and unpredictability are two core problems in pervasive computing.
The challenge is to seamlessly integrate devices with varying computational resources in a …