提出一种压缩优化方法，在联邦学习中实现超99%模型压缩，精度损失低于1.18%。
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis
- 服务器端对本地模型进行剪枝压缩，降低通信与存储开销。
- 在工业时序数据异常检测与缺失值填补任务中，压缩率超99.7%，性能损失<1.18%。
- 适合资源受限的工业物联网场景，兼顾隐私保护与高效推理。
工业应用中的异常与缺失数据问题日益突出。近年来，深度学习虽提升了异常检测精度，但依赖大模型导致存储与计算成本上升。边缘设备数据涉及用户隐私，联邦学习（FL）可有效解决隐私问题，但其模型训练与传输带来更高的通信开销。为此，本文提出一种基于压缩的服务器端优化方法，接收本地模型后进行剪枝并生成更紧凑的全局模型。在异常检测与缺失值填补任务上的实验表明，该方案相比集中式方法实现超过99.7%的压缩率，性能损失不足1.18%，显著降低处理、存储与通信复杂度。
原文摘要 · Abstract (English)
Anomaly and missing data constitute a thorny problem in industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy is achieved with the utilization of large neural networks, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the privacy-preserving distributed paradigm, known as federated learning (FL). This framework allows edge devices to train and exchange models increasing also the communication cost. Thus, to deal with the increased communication, processing and storage challenges of the FL based deep anomaly detection NN pruning is expected to have significant benefits towards reducing the processing, storage and communication complexity. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that fusses the received local models broadcast and performs pruning generating a more compressed model. Experiments in the context of anomaly detection and missing value imputation demonstrate that the proposed FL scenario along with the proposed compressed-based method are able to achieve high compression rates (more than $99.7\%$) with negligible performance losses (less than $1.18\%$ ) as compared to the centralized solutions.
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