CFL-ICCV: Clustered federated learning framework with an intra-cluster cross-validation mechanism for DER forecasting

Linbin Liu, June Li and Juan Wang

Applied Energy, 2025, vol. 377, issue PD, No S0306261924020828

Abstract: Federated learning (FL) enables collaborative local training of forecasting models for distributed energy resources (DERs), thereby safeguarding the original energy data from exposure, which may contain user privacy or business secrets. However, traditional FL for DERs forecasting faces two significant challenges: (1) DERs data is typically non-independent and identically distributed (non-IID), leading to poor forecasting performance. To address this issue, recent research has explored clustering FL. However, existing approaches often require clients to submit data for centralized clustering, ignoring privacy protection of clustering data. (2) DERs clients are vulnerable to Byzantines attacks. We propose a novel FL framework for DERs forecasting, named Clustered Federated Learning with an Intra-cluster Cross-validation mechanism (CFL-ICCV), to enhance both accuracy and security while preserving the data privacy of DERs clients. Within CFL-ICCV, DERs clients engage in distributed clustering by secret sharing, eliminating the need to submit original data. The ICCV mechanism ensures that local updates are validated by clients not participating in the current training round, allowing the identification and removal of malicious local models. Additionally, we design a parallel mode for ICCV, significantly reducing its associated overhead. Experimental results on the real-world dataset BDG2 demonstrate that, compared to baselines, CFL-ICCV reduces prediction error by 12.90% while protecting data privacy, and shows greater robustness against various Byzantine attacks by reducing prediction error at most 86.45% under attacks.

Keywords: Distributed energy resource (DER) forecasting; Federated learning (FL); Byzantine robustness; Clustering; Cross-validation (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924020828
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924020828

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124699

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().