# Uncertainty-Aware Federated Learning for Cyber-Resilient Microgrid Energy Management

arXiv: https://arxiv.org/abs/2511.17968 · HTML: https://mlsanity.com/paper/2511.17968/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 1 of 1)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)

## Abstract (arXiv)

Maintaining economic efficiency and operational reliability in microgrid energy management systems under cyberattack conditions remains challenging. Most approaches assume non-anomalous measurements, make predictions with unquantified uncertainties, and do not mitigate malicious attacks on renewable forecasts for energy management optimization. This paper presents a comprehensive cyber-resilient framework integrating federated Long Short-Term Memory-based photovoltaic forecasting with a novel two-stage cascade false data injection attack detection and energy management system optimization. The approach combines autoencoder reconstruction error with prediction uncertainty quantification to enable attack-resilient energy storage scheduling while preserving data privacy. Extreme false data attack conditions were studied that caused 58% forecast degradation and 16.9% operational cost increases. The proposed integrated framework reduced false positive detections by 70%, recovered 93.7% of forecasting performance losses, and achieved 5% operational cost savings, mitigating 34.7% of attack-induced economic losses. Results demonstrate that precision-focused cascade detection with multi-signal fusion outperforms single-signal approaches, validating security-performance synergy for decentralized microgrids.