TS-ACL: A Time Series Analytic Continual Learning Framework for Privacy-Preserving and Class-Incremental Pattern Recognition

• URL: http://arxiv.org/abs/2410.15954v2
• Date: Mon, 18 Nov 2024 07:30:06 GMT
• Title: TS-ACL: A Time Series Analytic Continual Learning Framework for Privacy-Preserving and Class-Incremental Pattern Recognition
• Authors: Kejia Fan, Jiaxu Li, Songning Lai, Linpu Lv, Anfeng Liu, Jianheng Tang, Houbing Herbert Song, Yutao Yue, Huiping Zhuang,
• Abstract summary: TS-ACL is a novel framework for privacy-preserving and class-incremental pattern recognition. It transforms each update of the model into a gradient-free analytical learning process with a closed-form solution. It simultaneously achieves non-forgetting, privacy preservation, and lightweight consumption, making it widely suitable for various applications.
• Score: 14.108911377558242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Class-incremental pattern recognition in time series is a significant problem, which aims to learn from continually arriving streaming data examples with incremental classes. A primary challenge in this problem is catastrophic forgetting, where the incorporation of new data samples causes the models to forget previously learned information. While the replay-based methods achieve promising results by storing historical data to address catastrophic forgetting, they come with the invasion of data privacy. On the other hand, the exemplar-free methods preserve privacy but suffer from significantly decreased accuracy. To address these challenges, we proposed TS-ACL, a novel Time Series Analytic Continual Learning framework for privacy-preserving and class-incremental pattern recognition. Identifying gradient descent as the root of catastrophic forgetting, TS-ACL transforms each update of the model into a gradient-free analytical learning process with a closed-form solution. By leveraging a pre-trained frozen encoder for embedding extraction, TS-ACL only needs to recursively update an analytic classifier in a lightweight manner. This way, TS-ACL simultaneously achieves non-forgetting, privacy preservation, and lightweight consumption, making it widely suitable for various applications, particularly in edge computing scenarios. Extensive experiments on five benchmark datasets confirm the superior and robust performance of TS-ACL compared to existing advanced methods. Code is available at https://github.com/asdasdczxczq/TS-ACL.

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