Abstract: For the health management of batteries in new energy vehicles, it is essential to collaboratively share distributed battery data and establish a federated learning model to extract valuable information. To counteract the privacy leakage risks associated with battery data sharing, this paper designs a taskindependent privacy protection and communicationefficient federated learningempowered edge intelligence model. This model learns personalized subnetworks that generalize well to local data and uses network pruning to find the optimal subnetwork, ensuring inference accuracy. Meanwhile, to resist feature reconstruction attacks and privacy leakage risks, it constructs taskindependent privacyprotective anonymous intermediate representations. By employing adversarial training, it maximizes the reconstruction error of the adversarial reconstructor and the classification error of the adversarial classifier, while minimizing the classification error of the target classifier. Experimental simulations show that this method improves inference accuracy by 8.85%  and reduces communication overhead by 1.95 times. The balance analysis of utility and privacy demonstrates that it ensures the accuracy of target feature extraction while protecting privacy.

Key words: privacy protection, federated learning, feature extraction, battery monitoring, network pruning

摘要： 新能源汽车的电池健康管理必须将分布式电池数据进行协作共享，建立联邦学习模型提取有价值的信息.为了抵御电池数据共享面临的隐私泄露，设计一种任务无关的隐私保护和通信高效的联邦学习赋能边缘智能模型.学习个性化子网络对本地数据进行良好的泛化，借助网络剪枝寻找最优子网络确保推理精度.同时，为抵御特征重构攻击和隐私泄露风险，构造任务无关的隐私保护匿名中间表示，通过对抗性训练最大化对抗重构器的重构误差和对抗分类器的分类误差，并最小化目标分类器的分类误差.实验仿真表明，该方法的推理精度提高了8.85%，并节省了1.95倍的通信开销.采用效用和隐私的平衡分析表明，在保护隐私的同时确保目标特征提取的准确度.

关键词: 隐私保护, 联邦学习, 特征提取, 电池监测, 网络剪枝