Federated Anomaly Detection and Mitigation for EV Charging Forecasting Under Cyberattacks

TL;DR

This paper introduces a federated learning framework for EV charging demand forecasting that detects and mitigates cyberattacks while preserving data privacy, demonstrating improved accuracy and resilience under adversarial conditions.

Contribution

It proposes a novel anomaly-resilient federated learning framework combining LSTM autoencoders, data interpolation mitigation, and collaborative LSTM models for secure EV demand prediction.

Findings

15.2% improvement in R2 accuracy over centralized models

Recovered 47.9% of attack-induced performance degradation

Achieved 91.3% precision with 1.21% false positives

Abstract

Electric Vehicle (EV) charging infrastructure faces escalating cybersecurity threats that can severely compromise operational efficiency and grid stability. Existing forecasting techniques are limited by the lack of combined robust anomaly mitigation solutions and data privacy preservation. Therefore, this paper addresses these challenges by proposing a novel anomaly-resilient federated learning framework that simultaneously preserves data privacy, detects cyber-attacks, and maintains trustworthy demand prediction accuracy under adversarial conditions. The proposed framework integrates three key innovations: LSTM autoencoder-based distributed anomaly detection deployed at each federated client, interpolation-based anomalous data mitigation to preserve temporal continuity, and federated Long Short-Term Memory (LSTM) networks that enable collaborative learning without centralized data aggregation. The framework is validated on real-world EV charging infrastructure datasets combined with real-world DDoS attack datasets, providing robust validation of the proposed approach under realistic threat scenarios. Experimental results demonstrate that the federated approach achieves superior performance compared to centralized models, with 15.2% improvement in R2 accuracy while maintaining data locality. The integrated cyber-attack detection and mitigation system produces trustworthy datasets that enhance prediction reliability, recovering 47.9% of attack-induced performance degradation while maintaining exceptional precision (91.3%) and minimal false positive rates (1.21%). The proposed architecture enables enhanced EV infrastructure planning, privacy-preserving collaborative forecasting, cybersecurity resilience, and rapid recovery from malicious threats across distributed charging networks.