Centralised vs. Decentralised Federated Load Forecasting: Who Holds the Key to Adversarial Attack Robustness?

The integration of AI and ML in energy forecasting is pivotal for modern energy management. Federated Learning (FL) stands out by enhancing data privacy and collaboration among distributed energy resources, enabling distributed model training while reducing reliance on centralized servers and data transfers. Despite its merits, FL faces substantial security challenges, particularly from adversarial attacks that can compromise the integrity and reliability of the models. This paper aims to address these security concerns by examining the efficiency of Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL) for distributed load forecasting. Through comparative analysis utilizing publicly available household datasets for short-term load forecasting, our study reveals that DFL demonstrates superior resilience against adversarial attacks compared to CFL. Notably, our findings indicate that the impact of adversarial model poisoning attacks is confined to the targeted client in DFL, while CFL exhibits broader susceptibility across all clients. When attacked, CFL's averaged client Mean Absolute Error (MAE) increased from 0.076 to 0.22 kWh, whereas DFL maintained a lower MAE of 0.116 kWh. Additionally, we present Decentralized Random Layer Aggregation (DRLA) to augment DFL's robustness, offering further insights into enhancing FL methodologies within energy contexts.