Robust and Communication-Efficient Personalized Federated Learning for False Power Consumption Detection in Heterogeneous Smart Grids

Detecting false power consumption data injection (FDI) attacks requires utilities to collaborate without sharing consumers’ raw data. Federated learning (FL) enables such collaboration. However, conventional FL exchanges complete models in every round and loses accuracy when the utilities’ data are not independent and identically distributed (non-IID). It also produces a shared detector that a malicious participant can exploit to craft evasion attacks. This paper proposes a personalized federated learning (PFL) approach in which utilities jointly train convolutional feature-extraction layers while keeping their classification layers local. Adversarial examples are incorporated into local training to improve resistance to evasion. The approach requires a single model per utility and no public dataset at the server. We evaluate it on the Ausgrid and Irish Smart Energy Trial datasets, with utilities that differ in both consumption patterns and attack characteristics. Compared with conventional FL, the proposed approach reduces the data exchanged per round and utility from 306.63 KB to 3.50 KB. With adversarial training, it improves accuracy by up to 23.75 and 29.82 percentage points on Ausgrid and ISET, respectively, and lowers the success rates of the evaluated FGSM and BIM attacks. These results show that personalization can ease the accuracy–robustness trade-off while substantially reducing communication costs.