FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Environments
FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Environments
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DOI：
10.1145/3576841.3585921
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发表时间：
2023-05
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影响因子：
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通讯作者：
Ashish Gupta;Hari Prabhat Gupta;Sajal K. Das
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作者：
Ashish Gupta;Hari Prabhat Gupta;Sajal K. Das
随着人们生活水平的提高和网络物理系统的快速发展，居住环境正在变得智能化和互联互通，导致整体能源消耗大幅上升。由于家用电器是主要的能源消耗设备，准确识别家用电器对于避免无人值守的使用和最大限度地减少智能电网的高峰负荷，从而节约能源和使智能环境更可持续变得至关重要。传统上，家电识别模型是在中央服务器(服务提供商)通过智能插头从客户端(消费者)收集用电数据来训练的，这导致了隐私泄露。此外，当设备连接到非指定的智能插头时，这些数据很容易受到噪音标签的影响。在共同解决这些问题的同时，我们提出了一种新颖的联合学习方法，称为Fedar+，即使在错误标记的训练数据的情况下，也能够以保护隐私的方式跨客户进行分散的模型训练。Fedar+引入了一种自适应噪声处理方法，本质上是一个结合了权重和标签分布的联合损失函数，使设备识别模型能够对抗噪声标签。通过在公寓楼中部署智能插头，我们收集了一个带标签的数据集，以及两个现有的数据集，用于评估Fedar+的性能。实验结果表明，我们的方法可以有效地处理高达30%的噪声标签浓度，同时在准确率上比以前的方法有很大的提高。
With the enhancement of people's living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. As household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels that may appear when an appliance gets connected to a non-designated smart plug. While addressing these issues jointly, we propose a novel federated learning approach to appliance recognition, called FedAR+, enabling decentralized model training across clients in a privacy-preserving way even with mislabeled training data. FedAR+ introduces an adaptive noise handling method, essentially a joint loss function incorporating weights and label distribution, to empower the appliance recognition model against noisy labels. By deploying smart plugs in an apartment complex, we collect a labeled dataset that, along with two existing datasets, are utilized to evaluate the performance of FedAR+. Experimental results show that our approach can effectively handle up to 30% concentration of noisy labels while outperforming the prior solutions by a large margin on accuracy.