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

Privacy-preserving load forecasting in smart grids using federated learning: a comparative analysis of aggregation strategies

Accurate load forecasting plays a vital role in optimizing energy distribution and integrating renewable energy sources within smart grid systems. However, traditional centralized deep learning approaches present major challenges related to data privacy, communication overhead, and scalability, particularly in scenarios involving distributed energy consumers. To address these concerns, this study proposes a federated learning (FL) framework that leverages Gated Recurrent Unit (GRU) networks to enable decentralized and privacy-preserving load forecasting. The proposed approach is evaluated using three distinct aggregation strategies: Federated Averaging (FedAvg), Federated Proximal (FedProx), and Federated Averaging with Trimmed Mean (FedTrimmedAvg). These methods aim to alleviate data heterogeneity and client drift, which are prevalent in non-independent and identically distributed (non-IID) settings commonly encountered in smart grid environments—challenges known to limit the effectiveness of standard FedAvg and that motivate the need for more robust alternatives. Experimental results on real-world energy consumption datasets demonstrate that the proposed FL framework achieves competitive forecasting accuracy while preserving client data privacy. A rigorous comparative analysis reveals that FedProx and FedTrimmedAvg consistently outperform FedAvg under non-IID conditions, with FedTrimmedAvg offering the highest robustness to outliers and inconsistent client behavior. These findings highlight the effectiveness of robust aggregation techniques in federated settings and present a scalable, privacy-aware solution for intelligent energy management in next-generation smart grid infrastructures.

Regime-Aware Federated Aggregation (RAFA) for privacy-preserving energy load forecasting across heterogeneous European grid clients

RAFA (Regime-Aware Federated Aggregation), a novel FL framework that explicitly detects and adapts to distribution shifts in client model updates without accessing raw client data, provides a lightweight, privacy-preserving extension of standard federated aggregation that is robust to regime shifts in energy systems.

An Investigation of Federated Learning for Air Quality Forecasting and Monitoring

Air quality, as a global issue, impacts people’s health and daily life. It requires precise prediction and monitoring for sustainable urban management. Conventional centralized air quality prediction methods are limited by data privacy, high communication costs, and low scalability in distributed environments. Federated learning offers a solution that collaboratively trains global models without sharing raw data. The passage conducted a comprehensive literature analysis to categorize FL applications in air quality research into four technical streams: Probabilistic Graphical Models (Federated Bayesian Networks) for causal inference; Time-series Deep Learning (integrating LSTM/CNN with FedAvg/FedProx) for temporal pattern extraction; Spatio-temporal Graph Neural Networks (GC-LSTM) for capturing complex spatial dependencies; and Multi-model Ensemble with Transfer Learning for heterogeneous client adaptation. Nevertheless, there are significant gaps that remain in terms of model interpretability and the ability to generalize across climate variations. All in all, this article provides a relatively comprehensive overview of the application of federated learning in air quality forecasting and monitoring. It will assist researchers in advancing this field in the future.

Entropy-Regularized Weighted Federated Meta-Learning Approach for Power Load Forecasting in Non-IID Scenarios

A Causal-Driven Hierarchical Decentralised Federated Learning Framework for Resilient Load Forecasting in Distributed Microgrids

Modern microgrids require distributed intelligence and edge computing to handle variable demand and renewable generation, but heterogeneity, communication limits, and privacy hinder centralised forecasting. This paper proposes a causally guided hierarchical decentralised federated learning (H-DFL) framework for resilient short-term load forecasting, integrating a hybrid TCN–BiLSTM with MCMC-based probabilistic causal feature selection. A three-tier architecture enables local training and hierarchical aggregation without raw data sharing, improving scalability and communication efficiency through sparse, interpretable feature selection driven by key factors such as solar and weather dynamics. Experiments on the Ausgrid dataset show improved stability and efficiency over Granger Causality (GC), Dynamic Causal Modeling (DCM), and Markov chain Monte Carlo (MCMC) baselines, with intervention tests confirming robustness under solar, demand, and outage disturbances. Overall, the results demonstrate that combining probabilistic feature sparsity with hierarchical decentralised federated learning to enable scalable, privacy-preserving, and resilient load forecasting for future microgrid systems.

On the Impact of Data Heterogeneity in Federated Learning: A Case Study on Air Quality Prediction

Federated learning has become a popular model to apply in privacy-preserving modeling in distributed settings, particularly when the models are applied to data that is distributed among various locations and is often sensitive, such as in air quality prediction. This paper looks into how effective federated learning is for predicting ozone (O₃) concentrations, under both independent and non-independent data distributions. In particular, two representative algorithms Federated Averaging (FedAvg)and Federated Averaging (FedAvg) are experimented on a real-world air quality dataset. An experimental framework was established that was relatively comprehensive and centralized training and local-only models were used as baselines. Mean absolute error (MAE) and root mean squared error (RMSE) are used to measure model performance. The findings suggest that federated learning performs much better than the isolated local models and the performance is similar to that of the centralized training. Having said that, data heterogeneity does present certain issues-it slows down convergence and decreases accuracy in prediction. In such non-IID conditions, FedProx is more stable and less erroneous than FedAvg implying that it is more resistant to client drift.Most importantly, this paper provides empirical data on the impact of data heterogeneity on federated learning, and proves that federated learning can be a viable alternative in privacy-sensitive environmental prediction problems.