# Byzantine-Resilient Distributed P2P Energy Trading via Spatial-Temporal Anomaly Detection

arXiv: https://arxiv.org/abs/2505.20567 · HTML: https://mlsanity.com/paper/2505.20567/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2025.

## Built on by (most cited first, 1 of 1)

- [Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data](https://mlsanity.com/paper/2507.14999/) (2025)

## Abstract (arXiv)

Distributed peer-to-peer (P2P) energy trading mandates an escalating coupling between the physical power network and communication network, necessitating high-frequency sharing of real-time data among prosumers. However, this data-sharing scheme renders the system vulnerable to various malicious behaviors, as Byzantine agents can initiate cyberattacks by injecting sophisticated false data. To better investigate the impacts of malicious Byzantine faults, this paper develops a fully distributed P2P energy trading model by accounting for the high-fidelity physical network constraints. To further detect Byzantine faults and mitigate their impacts on distributed P2P energy trading problem, we propose an online spatial-temporal anomaly detection approach by leveraging the tensor learning method, which is informed by the domain knowledge to enable awesome detection performance. Moreover, to enhance its computational efficiency, we further develop closed-form solutions for the proposed detection approach. Subsequently, we derive theoretical conditions for guaranteeing optimality and convergence of the distributed P2P energy trading problem with anomaly detection mechanisms. Results from numerical simulations validate the effectiveness, optimality, and scalability of the proposed approach.