用联邦聚类平均算法保护用户隐私，提升能源管理模型精度
Privacy Protection in Prosumer Energy Management Based on Federated Learning
- 通过聚类分层采样与参数加权平均解决数据非独立同分布问题
- 在非独立同分布下模型准确率提升，通信轮次显著减少
- 适合关注隐私保护的智能电网与分布式能源系统研究者
随着产消者（prosumer）的快速发展，亟需构建能充分利用其灵活性并兼顾各方利益的能源管理系统。然而，系统建设可能暴露用户隐私。本文提出联邦聚类平均（FedClusAvg）算法，通过客户端进行聚类分层采样与多次本地迭代，根据参数偏离均值程度确定子服务器参数的加权平均，并采用三层次框架实现多轮本地更新后上传。该算法有效缓解了联邦学习中的非独立同分布（Non-IID）问题，提升了模型在非独立同分布场景下的准确性，同时通过本地多次迭代与分层结构显著降低通信轮次。
原文摘要 · Abstract (English)
With the booming development of prosumers, there is an urgent need for a prosumer energy management system to take full advantage of the flexibility of prosumers and take into account the interests of other parties. However, building such a system will undoubtedly reveal users' privacy. In this paper, by solving the non-independent and identical distribution of data (Non-IID) problem in federated learning with federated cluster average(FedClusAvg) algorithm, prosumers' information can efficiently participate in the intelligent decision making of the system without revealing privacy. In the proposed FedClusAvg algorithm, each client performs cluster stratified sampling and multiple iterations. Then, the average weight of the parameters of the sub-server is determined according to the degree of deviation of the parameter from the average parameter. Finally, the sub-server multiple local iterations and updates, and then upload to the main server. The advantages of FedClusAvg algorithm are the following two parts. First, the accuracy of the model in the case of Non-IID is improved through the method of clustering and parameter weighted average. Second, local multiple iterations and three-tier framework can effectively reduce communication rounds.
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