Effective Communication Compression Framework Using Federated Learning Privacy Model for Smart Grid

The rapidly increasing number of electricity usage in smart grid is generating huge quantities of data in end devices. The generated electricity data has benign data and false data and these are collectively stored in local servers. To maintain data privacy on local data the Federated learning framework is approached. The divided local datasets are processed by local model to generate local parameters. All these local model’s parameters are communicated to the global model with digital sovereignty, compression for aggregation. Global model performs the common modelling and generates the global parameter; this is sent to the local model to update their parameter for the next iteration of modelling. The challenge in increased electricity data creates the complexity in communicating the local model parameter to the global model. The global model accuracy is maintained same when local model parameters are sparsified and quantized again in global model parameters are dequantized. This work proposes the effective communication compression framework with FedAvg Framework (ECC-FedAvg) with compression techniques, which increases the communication effectiveness and increases the communication efficiency in smart grid.