# FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction

arXiv: https://arxiv.org/abs/2309.10337 · HTML: https://mlsanity.com/paper/2309.10337/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 62 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework](https://mlsanity.com/paper/2002.10671/) (2020)
- [FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia](https://mlsanity.com/paper/2104.12581/) (2021)
- [Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting](https://mlsanity.com/paper/2111.07462/) (2021)
- [Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach](https://mlsanity.com/paper/2102.12245/) (2021)
- [Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition](https://mlsanity.com/paper/2205.14315/) (2022)
- [A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning](https://mlsanity.com/paper/2103.11870/) (2021)
- [DER Forecast using Privacy Preserving Federated Learning](https://mlsanity.com/paper/2107.03248/) (2021)
- [Average Convergence Rate of Evolutionary Algorithms](https://mlsanity.com/paper/1504.08117/) (2015)

## Built on by (most cited first, 1 of 1)

- [Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data](https://mlsanity.com/paper/2507.14999/) (2025)

## Abstract (arXiv)

Privacy is important when dealing with sensitive personal information in machine learning models, which require large data sets for training. In the energy field, access to household prosumer energy data is crucial for energy predictions to support energy grid management and large-scale adoption of renewables however citizens are often hesitant to grant access to cloud-based machine learning models. Federated learning has been proposed as a solution to privacy challenges however report issues in generating the global prediction model due to data heterogeneity, variations in generation patterns, and the high number of parameters leading to even lower prediction accuracy. This paper addresses these challenges by introducing FedWOA a novel federated learning model that employs the Whale Optimization Algorithm to aggregate global prediction models from the weights of local LTSM neural network models trained on prosumer energy data. The proposed solution identifies the optimal vector of weights in the search spaces of the local models to construct the global shared model and then is subsequently transmitted to the local nodes to improve the prediction quality at the prosumer site while for handling non-IID data K-Means was used for clustering prosumers with similar scale of energy data. The evaluation results on prosumers energy data have shown that FedWOA can effectively enhance the accuracy of energy prediction models accuracy by 25% for MSE and 16% for MAE compared to FedAVG while demonstrating good convergence and reduced loss.