[HTML][HTML] Adaptive single-layer aggregation framework for energy-efficient and privacy-preserving load forecasting in heterogeneous Federated smart grids
Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …
data from distributed load networks while ensuring data privacy. However, the …
[HTML][HTML] A survey of security strategies in federated learning: Defending models, data, and privacy
Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training across multiple devices while preserving data …
enabling decentralized model training across multiple devices while preserving data …
[HTML][HTML] Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?
The integration of AI and ML into energy forecasting is crucial for modern energy
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
Electric load forecasting under false data injection attacks via denoising deep learning and generative adversarial networks
Accurate electric load forecasting at various time periods is considered a necessary
challenge for electricity consumers and generators to maximize their economic efficiency in …
challenge for electricity consumers and generators to maximize their economic efficiency in …
Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan
A continuous supply of electricity is necessary to maintain an acceptable standard of life,
and the power distribution system's overhead line components play a crucial role in this …
and the power distribution system's overhead line components play a crucial role in this …
Defending federated learning from backdoor attacks: Anomaly-aware fedavg with layer-based aggregation
Federated Learning (FL) is susceptible to backdoor adversarial attacks during the training
process, which poses a significant threat to the model's performance. Existing adversarial …
process, which poses a significant threat to the model's performance. Existing adversarial …
[PDF][PDF] Lightweight single-layer aggregation framework for energy-efficient and privacy-preserving load forecasting in heterogeneous smart grids
Federated Learning (FL) in load forecasting improves predictive accuracy by leveraging
data from distributed load networks while preserving data privacy. However, the …
data from distributed load networks while preserving data privacy. However, the …
Mobility-aware federated learning-based proactive UAVs placement in emerging cellular networks
With the vast proliferation of smart mobile devices, there is an ever-increasing demand for
higher data rates and seamless connectivity throughout. Current 5th generation and beyond …
higher data rates and seamless connectivity throughout. Current 5th generation and beyond …
A Tutorial on Non-Terrestrial Networks: Towards Global and Ubiquitous 6G Connectivity
The International Mobile Telecommunications (IMT)-2030 framework recently adopted by
the International Telecommunication Union Radiocommunication Sector (ITU-R) envisions …
the International Telecommunication Union Radiocommunication Sector (ITU-R) envisions …
Sustainable and lightweight defense framework for resource constraint federated learning assisted smart grids against adversarial attacks
Energy networks face challenges in managing and securing the vast data generated by
smart grids. Federated Learning (FL) offers a cost-effective, privacy-aware solution for model …
smart grids. Federated Learning (FL) offers a cost-effective, privacy-aware solution for model …