Federated Learning based Energy Demand Prediction with Clustered Aggregation

• URL: http://arxiv.org/abs/2210.15850v1
• Date: Fri, 28 Oct 2022 02:46:59 GMT
• Title: Federated Learning based Energy Demand Prediction with Clustered Aggregation
• Authors: Ye Lin Tun, Kyi Thar, Chu Myaet Thwal, Choong Seon Hong
• Abstract summary: Energy usage information collected by the clients' smart homes can be used to train a deep neural network to predict the future energy demand. In this paper, we propose a recurrent neural network based energy demand predictor trained with federated learning on clustered clients to take advantage of distributed data.
• Score: 14.631304404306778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To reduce negative environmental impacts, power stations and energy grids need to optimize the resources required for power production. Thus, predicting the energy consumption of clients is becoming an important part of every energy management system. Energy usage information collected by the clients' smart homes can be used to train a deep neural network to predict the future energy demand. Collecting data from a large number of distributed clients for centralized model training is expensive in terms of communication resources. To take advantage of distributed data in edge systems, centralized training can be replaced by federated learning where each client only needs to upload model updates produced by training on its local data. These model updates are aggregated into a single global model by the server. But since different clients can have different attributes, model updates can have diverse weights and as a result, it can take a long time for the aggregated global model to converge. To speed up the convergence process, we can apply clustering to group clients based on their properties and aggregate model updates from the same cluster together to produce a cluster specific global model. In this paper, we propose a recurrent neural network based energy demand predictor, trained with federated learning on clustered clients to take advantage of distributed data and speed up the convergence process.

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