Edge Smart Meter Based LSTM Federated Learning With AES Cryptographic Blockchain for Smart Grid AIoT Networks

The Smart Grid Artificial Intelligence-of-Things (SG-AIoT) uses SCADA (Supervisory Control and Data Acquisition) and AMI (Advanced Metering Infrastructure) to improve electricity distribution. However, the current SG-AIoT system faces challenges such as inaccurate energy forecasting, insecure data computation, information leakage, and data CIA (Confidentiality, Integrity, and Availability) issues. Smart-meter data has been provided to drive initiatives such as the UK’s "Low-Carbon-London" project, where the need for accurate forecasting and grid data security is felt. To address these issues, this paper proposes a secure federated learning framework that combines LSTM (Long Short-Term Memory) neural networks with AES (Advanced Encryption Standard) cryptographic blockchain across three-tier grid layers (Edge smart meters, Fog SCADA servers, and Cloud server). An AES-based blockchain framework is developed to secure data communication across all grid layers—edge, fog, and cloud. At the IoT smart meter edge nodes, a double-layered LSTM and dropout neural network is used for accurate energy demand forecasting. Forecasted data from each edge node is encrypted and aggregated at the fog layer using the AES blockchain. The proposed CBFedAggSum (Cryptographic Blockchain with Federated Learning Aggregation through Summation) method enables secure and efficient model aggregation at fog and cloud nodes. This end-to-end process strengthens grid data security and improves forecasting accuracy, effectively addressing key challenges in SG-AIoT systems. We compared our approach with six existing methods (FedAvg, FedSGD, SecAgg, 1D-CNN-GRU, PP-CE-FL, and Edge-Cloud-AIoT-FL). We found that our model outperformed them in forecasting accuracy and privacy preservation.