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.

翻译：在机器学习模型中处理敏感个人信息时，隐私保护至关重要，因为这些模型需要大量数据集进行训练。在能源领域，获取家庭产消者的能源数据对于支持电网管理和可再生能源大规模应用的能源预测至关重要，但公民往往不愿授权云基机器学习模型访问这些数据。联邦学习已被提出作为隐私挑战的解决方案，但存在因数据异质性、发电模式差异以及参数数量过多导致全局预测模型生成困难、预测准确度更低的问题。本文通过引入FedWOA（一种新型联邦学习模型）来应对这些挑战，该模型采用鲸鱼优化算法，从基于产消者能源数据训练的本地LSTM神经网络模型权重中聚合全局预测模型。所提方案在本地模型的搜索空间中识别最优权重向量以构建全局共享模型，随后将其传输至本地节点以提高产消者站点的预测质量，同时为了处理非独立同分布数据，采用K-Means对具有相似能源数据规模的产消者进行聚类。在产消者能源数据上的评估结果表明，与FedAVG相比，FedWOA可将能源预测模型的准确率有效提升25%（MSE指标）和16%（MAE指标），同时展现出良好的收敛性和更低的损失值。