Mitigating cyber anomalies in virtual power plants using artificial-neural-network-based secondary control with a federated learning-trust adaptation

SI Taheri, M Davoodi, MH Ali - Energies, 2024 - mdpi.com
Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive
communication layer. FL-trust, an improved federated learning (FL) approach, has been …

Optimizing efficient personalized federated learning with hypernetworks at edge

R Zhang, Y Chen, C Wu, F Wang, J Liu - IEEE Network, 2023 - ieeexplore.ieee.org
The recent advances in 5G and mobile edge computing facilitate the rapid development of
the Internet of Things (IoT), enabling collective intelligence with data support from a massive …

QARMA-FL: Quality-aware robust model aggregation for mobile crowdsourcing

S Edirimannage, C Elvitigala, I Khalil… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Over the past few years, the improved detection and processing features of Internet of
Things (IoT) devices have opened the doors to several mobile crowdsourcing (MC) …

Differential privacy hierarchical federated learning method based on privacy budget allocation

W Yuwen, G Yu, L **angjun - 2023 9th International …, 2023 - ieeexplore.ieee.org
Federated learning can effectively protect users' personal data from being obtained by
attackers, and differential privacy can enhance the privacy of federated learning and solve …

Defense Strategy against Byzantine Attacks in Federated Machine Learning: Developments towards Explainability

N Rodríguez-Barroso, J Del Ser… - … on Fuzzy Systems …, 2024 - ieeexplore.ieee.org
The rise of high-risk AI systems has led to escalating concerns, prompting regulatory efforts
such as the recently approved EU AI Act. In this context, the development of responsible AI …

TS-FedNBS: Federated Edge Computing with Enhanced Robustness

A Basharat, R Wang, P Xu - 2024 International Conference on …, 2024 - ieeexplore.ieee.org
Edge-assisted federated learning (FedEdge) that integrates an intermediate layer of edge
nodes to reduce the workload for central server in traditional federated learning systems has …