Tabular data streams are increasingly prevalent in real-time decision-making across healthcare, finance, and the Internet of Things, often generated and processed on resource-constrained edge and mobile devices. Continual learning (CL) enables models to learn sequentially from such streams while retaining previously acquired knowledge. While recent CL advances have made significant progress in mitigating catastrophic forgetting, the energy and memory efficiency of CL for tabular data streams remains largely unexplored. To address this gap, we propose AttenMLP, which integrates attention-based feature replay with context retrieval and sliding buffer updates within a minibatch training framework for streaming tabular learning. We evaluate AttenMLP against state-of-the-art (SOTA) tabular models on real-world concept drift benchmarks with temporal distribution shifts. Experimental results show that AttenMLP achieves accuracy comparable to strong baselines without replay, while substantially reducing energy consumption through tunable design choices. In particular, with the proposed attention-based feature memory design, AttenMLP costs a 0.062 decrease in final accuracy under the incremental concept drift dataset, while reducing energy usage up to 33.3\% compared to TabPFNv2. Under the abrupt concept drift dataset, AttenMLP reduces 1.47\% energy consumption compared to TabR, at the cost of a 0.038 decrease in final accuracy. Although ranking third in global efficiency, AttenMLP demonstrates energy-accuracy trade-offs across both abrupt and incremental concept drift scenarios compared to SOTA tabular models.

翻译：表格数据流在医疗保健、金融和物联网等领域的实时决策中日益普遍，通常由资源受限的边缘和移动设备生成和处理。持续学习（CL）使模型能够从这类数据流中顺序学习，同时保留先前获得的知识。尽管近期的CL研究在缓解灾难性遗忘方面取得了显著进展，但针对表格数据流的CL在能量和内存效率方面的研究仍基本处于空白。为填补这一空白，我们提出了AttenMLP，该方法将基于注意力的特征回放与上下文检索及滑动缓冲区更新相结合，集成于一个用于流式表格学习的迷你批次训练框架中。我们在具有时间分布偏移的真实世界概念漂移基准上，将AttenMLP与最先进的（SOTA）表格模型进行了对比评估。实验结果表明，AttenMLP在不使用回放的情况下达到了与强基线模型相当的准确率，同时通过可调的设计选择显著降低了能耗。具体而言，采用所提出的基于注意力的特征记忆设计后，在增量概念漂移数据集下，AttenMLP的最终准确率仅下降0.062，但与TabPFNv2相比能耗降低了高达33.3%。在突变概念漂移数据集下，AttenMLP相比TabR降低了1.47%的能耗，代价是最终准确率下降0.038。尽管在全局效率中排名第三，但与SOTA表格模型相比，AttenMLP在突变和增量概念漂移场景下均展现出优异的能量-准确率权衡特性。