Federated Learning for Human Activity Recognition: Overview, Advances, and Challenges

O Aouedi, A Sacco, LU Khan… - IEEE Open Journal …, 2024 - ieeexplore.ieee.org
Human Activity Recognition (HAR) has seen remarkable advances in recent years, driven by
the widespread use of wearable devices and the increasing demand for personalized …

Communication overhead reduction in federated learning: a review

GS Nariman, HK Hamarashid - International Journal of Data Science and …, 2024 - Springer
Federated learning (FL) is a decentralized machine learning approach, where multiple
entities, typically devices or edge servers, collaboratively train a shared model while …

Carbon-aware machine learning: A case study on cellular traffic forecasting with spiking neural networks

T Tsiolakis, N Pavlidis, V Perifanis… - … Conference on Artificial …, 2024 - Springer
Cellular traffic forecasting is an essential task that enables network operators to perform
resource allocation and anomaly mitigation in fast-paced modern environments. However …

Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges

C Sandeepa, E Zeydan… - IEEE Open Journal …, 2024 - ieeexplore.ieee.org
The upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network
functionalities and services. Therefore, Machine Learning (ML) is essential for these …

FedLEC: Effective Federated Learning Algorithm with Spiking Neural Networks Under Label Skews

D Yu, X Du, L Jiang, S Bai, W Tong, S Deng - arxiv preprint arxiv …, 2024 - arxiv.org
With the advancement of neuromorphic chips, implementing Federated Learning (FL) with
Spiking Neural Networks (SNNs) potentially offers a more energy-efficient schema for …

Orchestration et optimisation du cache dans les réseaux IoT

SK Talatapeh - 2024 - theses.hal.science
Cette thèse explore l'amélioration des mécanismes de mise en cache au sein des réseaux
de l'Internet des Objets (IoT) et de l'Internet des Véhicules (IoV) afin d'atténuer la congestion …