mlsanityData to 7 Oct 2026

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

arXiv 2409.10764 ↗First posted 16 Sep 2024cs.LG cs.CRcited by 3

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Abstract (arXiv)

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. Furthermore, we discuss the gap between state-of-the-art (SOTA) FL research and its practical applications in SGs, and we propose future research directions. Unlike traditional surveys addressing security issues in centralized machine learning methods for SG systems, this survey is the first to specifically examine the applications and security concerns unique to FL-based SG systems. We also introduce FedGridShield, an open-source framework featuring implementations of SOTA attack and defense methods. Our aim is to inspire further research into applications and improvements in the robustness of FL-based SG systems.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 31 of its 128 references that are in the map. First posted on arXiv 16 Sep 2024.

Builds on

31 of 128 references.

References · 31

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  3. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
  4. 2018How To Backdoor Federated Learningcited by 745
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  6. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 561
  7. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  8. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  9. 2020Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networkscited by 341
  10. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 330
  11. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  12. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
19 more
  1. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 285
  2. 2022Neurotoxin: Durable Backdoors in Federated Learningcited by 97
  3. 2020Defending against Backdoors in Federated Learning with Robust Learning Ratecited by 82
  4. 2021SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsificationcited by 46
  5. 2022Differentially Private Federated Learning with Local Regularization and Sparsificationcited by 35
  6. 2022Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacycited by 32
  7. 2023Attention-Enhancing Backdoor Attacks Against BERT-based Modelscited by 32
  8. 2022AFLGuard: Byzantine-robust Asynchronous Federated Learningcited by 29
  9. 2021Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filteringcited by 28
  10. 2024Task-Agnostic Detector for Insertion-Based Backdoor Attackscited by 23
  11. 2020Voting-based Approaches For Differentially Private Federated Learningcited by 14
  12. 2024Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregationcited by 13
  13. 2022A Review of Federated Learning in Energy Systemscited by 9
  14. 2023Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Reviewcited by 7
  15. 2015Byzantine Attack and Defense in Cognitive Radio Networks: A Surveycited by 5
  16. 2019Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approachcited by 5
  17. 2021Cybersecurity in Power Grids: Challenges and Opportunitiescited by 3
  18. 2022Behind Closed Doors: Process-Level Rootkit Attacks in Cyber-Physical Microgrid Systemscited by 1
  19. 2023Privacy-Preserving Electricity Theft Detection based on Blockchaincited by 1

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyondcited by 6
  2. 2025Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Datacited by 0
  3. 2025Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Gridscited by 0