This guide provides guidance on the application of federated learning and neural network-based models for electric energy consumption data acquisition and analysis. It describes the framework, processes, and methods for utilizing electric energy data in a secure and distributed artificial intelligence (AI) environment. The guide outlines mechanisms for data preprocessing, feature representation learning, model training with neural networks, parameter aggregation strategies, and security enhancement in federated learning scenarios.
- Standard Committee
- CIS/SC - Standards Committee
- Status
- Active PAR
- PAR Approval
- 2025-12-10
Working Group Details
- Society
- IEEE Computational Intelligence Society
- Standard Committee
- CIS/SC - Standards Committee
- Working Group
-
WGFLE2DA2/P3900 - Federated Learning of Power User Electric Energy Data Acquisition and Analysis Working Group
- IEEE Program Manager
- Patrycja Jarosz
Contact Patrycja Jarosz - Working Group Chair
- Yuan Chi
Other Activities From This Working Group
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