Benchmarking Data Heterogeneity Challenges in Federated Learning for Distributed Renewable Energy Forecasting: A Systematic Gap Analysis

Abstract

Federated Learning (FL) offers a privacy-preserving framework for training energy forecasting models across geographically distributed nodes without centralising raw metering data. However, the accuracy of classical FL algorithms degrades substantially when client data is heterogeneous, a condition that is the norm rather than the exception in distributed energy systems. This paper presents a novel systematic benchmark study quantifying accuracy degradation across four distinct heterogeneity axes: quantity skew (Dirichlet $\alpha$), covariate shift, temporal non-stationarity, and client availability. Here, five FL algorithms (FedAvg, FedProx, SCAFFOLD, Local-Only, and Centralized independent and identically distributed (IID) oracle) are evaluated with long short-term memory (LSTM) and Neural Basis Expansion Analysis for Time Series (N-BEATS) architectures across public datasets (NREL Solar, NREL Wind, Pecan Street, UK Smart Meter), generating over 190 experimental conditions. This degradation gap analysis reveals that standard FedAvg suffers up to 83.4% Mean Absolute Error (MAE) degradation relative to the centralised IID baseline under heavy quantity skew on solar data, with temporal non-stationarity emerging as the dominant single-axis contributor.