🤖 AI Summary
To address privacy leakage, degraded model accuracy, and excessive communication overhead caused by Non-IID data in prosumer energy management systems, this paper proposes FedClusAvg, a novel federated learning algorithm. FedClusAvg introduces clustered hierarchical sampling and parameter-deviation-weighted aggregation, operating within a three-tier architecture (central server–sub-server–client) that supports multi-round local training. Crucially, it ensures raw electricity consumption data remains on-device while significantly improving model accuracy under Non-IID conditions and reducing total communication rounds by over 40%. Its core contribution lies in jointly optimizing privacy preservation, modeling fidelity, and system efficiency—delivering a scalable, robust, and privacy-enhancing solution for distributed energy coordination and optimization.
📝 Abstract
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.