Asynchronous adaptive federated learning for distributed load forecasting with smart meter data

Abstract

Load forecasting is essential for the operation and planning of a utility company. Recent large-scale smart meter deployments enabled the collection of fine-grained load data and created opportunities for sensor-based load forecasting. Machine learning has achieved great successes in load forecasting; however, conventional machine learning techniques require data transfer to the cloud or another centralized location for model training. This not only exposes data to privacy and security risks but also, with a large number of smart meters, increases network traffic. Federated Learning (FL) has a potential to alleviate mentioned concerns by training a single ML model in a distributed manner without requiring participants to share their data. Consequently, this paper proposes FedNorm, a novel asynchronous FL approach for load forecasting with smart meter data. While most FL strategies are synchronous and require all clients to complete local training in each round of aggregation, FedNorm is asynchronous and aggregates updates without waiting for lagging clients. To achieve this, FedNorm measures the clients contributions considering similarities of local and global models as well as the loss function magnitudes. The experiments demonstrate that FedNorm achieves higher accuracy than seven state-of-the-art FL techniques. Moreover, experiments show that FedNorm converges in fewer communication rounds compared to other FL models.