Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities

2024-09-16

arXiv.org

AI Summary

To address security vulnerabilities and the deployment gap of federated learning (FL) in smart grids (SG), this paper systematically identifies FL application scenarios and domain-specific security risks across generation, transmission, distribution, and consumption. It reveals technical adaptation challenges and novel distributed attack surfaces inherent to cyber-physical SG environments. We propose a unified FL attack/defense framework tailored to SG requirements, together with an infrastructure roadmap that jointly ensures privacy preservation, robustness, and deployability. By establishing a rigorous mapping between FL security research and SG engineering practice, our work bridges a critical gap between theoretical FL advances and real-world energy system deployment. The study provides both a methodological foundation and practical guidelines for secure, trustworthy AI governance in smart grids.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The Smart Grid (SG) is a critical energy infrastructure that collects real-time electricity usage data to forecast future energy demands using information and communication technologies (ICT). Due to growing concerns about data security and privacy in SGs, federated learning (FL) has emerged as a promising training framework. FL offers a balance between privacy, efficiency, and accuracy in SGs by enabling collaborative model training without sharing private data from IoT devices. In this survey, we thoroughly review recent advancements in designing FL-based SG systems across three stages: generation, transmission and distribution, and consumption. Additionally, we explore potential vulnerabilities that may arise when implementing FL in these stages. Finally, we discuss the gap between state-of-the-art FL research and its practical applications in SGs and propose future research directions. These focus on potential attack and defense strategies for FL-based SG systems and the need to build a robust FL-based SG infrastructure. Unlike traditional surveys that address security issues in centralized machine learning methods for SG systems, this survey specifically examines the applications and security concerns in FL-based SG systems for the first time. Our aim is to inspire further research into applications and improvements in the robustness of FL-based SG systems.

Problem

• Explores federated learning applications in Smart Grid systems
• Investigates potential vulnerabilities in FL-based Smart Grid implementations
• Bridges gap between SOTA FL research and practical SG applications

Innovation

• Federated learning ensures privacy in Smart Grids
• FedGridShield framework implements attack and defense
• FL balances efficiency, accuracy, and data security