支持个性化隐私保护的异步联邦窃电检测方法

An Asynchronous Federated Learning Electricity Theft Detection Method for Personalized Privacy-preserving

窃电现象是新型电力系统所面临的重大挑战之一,当前主流的基于深度学习的窃电检测方法在数据集中处理过程中存在隐私泄露的风险,而本地数据处理又会因为样本数量不足导致模型泛化性较差。针对上述问题,文章提出一种支持个性化隐私保护的异步联邦窃电检测方法,在参与方用电数据不出本地的情况下完成联合建模。所提方案结合异步联邦训练方式和个性化差分隐私机制,设计兼顾延迟函数和隐私预算的异步聚合算法,有效平衡参与方个性化隐私保护需求与模型性能。实验证明所提方法可在实现个性化隐私保护的同时保证窃电检测模型的泛化性能,模型可较快收敛且收敛过程较为稳定。

Electricity theft is one of the major challenges in the new type power systems,the mainstream detection technology of electricity theft is based on deep learning,which has the risk of leaking privacy during centralized processing.Meanwhile,due to the insufficient number of samples,the models trained by local data face the problem of poor generalization and usability.Aiming to the above challenges,in this paper,we propose an electricity theft detection method based on asynchronous federated learning that supports personalized privacy protection,enabling the participants to model jointly without data leaving the local devices.Combining the asynchronous federated learning mode and the personalized privacy-preserving mechanism,an asynchronous aggregation algorithm considering both asynchronous staleness and the privacy budget difference is designed,achieving the balance of participants’personalized privacy-preserving requirements and model performance effectively.The experiments prove that the proposed method can achieve personalized privacy protection and ensure the generalization performance of the model at the same time.Meanwhile,the global model can converge rapidly and stably by utilizing our algorithm.