Machine learning models offer the capability to forecast future energy production or consumption and infer essential unknown variables from existing data. However, legal and policy constraints within specific energy sectors render the data sensitive, presenting technical hurdles in utilizing data from diverse sources. Therefore, we propose adopting a Swarm Learning (SL) scheme, which replaces the centralized server with a blockchain-based distributed network to address the security and privacy issues inherent in Federated Learning (FL)'s centralized architecture. Within this distributed Collaborative Learning framework, each participating organization governs nodes for inter-organizational communication. Devices from various organizations utilize smart contracts for parameter uploading and retrieval. Consensus mechanism ensures distributed consistency throughout the learning process, guarantees the transparent trustworthiness and immutability of parameters on-chain. The efficacy of the proposed framework is substantiated across three real-world energy series modeling scenarios with superior performance compared to Local Learning approaches, simultaneously emphasizing enhanced data security and privacy over Centralized Learning and FL method. Notably, as the number of data volume and the count of local epochs increases within a threshold, there is an improvement in model performance accompanied by a reduction in the variance of performance errors. Consequently, this leads to an increased stability and reliability in the outcomes produced by the model.

翻译：机器学习模型能够基于现有数据预测未来能源生产或消费，并推断关键未知变量。然而，特定能源领域的法律与政策约束使得数据具有敏感性，这为利用多源数据带来了技术障碍。为此，我们提出采用群体学习方案，该方案以基于区块链的分布式网络取代集中式服务器，以解决联邦学习集中式架构固有的安全与隐私问题。在此分布式协同学习框架中，每个参与机构管理用于组织间通信的节点。来自不同机构的设备利用智能合约进行参数上传与检索。共识机制确保了整个学习过程中的分布式一致性，保障了链上参数的透明可信性与不可篡改性。所提框架的有效性在三个真实世界能源序列建模场景中得到验证，其性能优于本地学习方法，同时相较于集中式学习与联邦学习方法，其数据安全性与隐私性也得到增强。值得注意的是，当数据量和本地训练轮次在阈值内增加时，模型性能有所提升，同时性能误差的方差减小。因此，这提高了模型输出结果的稳定性与可靠性。