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Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning
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本文提出CAMEL框架,解决现实场景中多数据流学习挑战,通过独立系统、动态专家池和多头注意力机制实现异构数据流间的协作学习,并通过AET策略动态调整专家生命周期,提高模型泛化能力和应对概念漂移的鲁棒性。

arXiv:2508.01598v1 Announce Type: cross Abstract: Learning from multiple data streams in real-world scenarios is fundamentally challenging due to intrinsic heterogeneity and unpredictable concept drifts. Existing methods typically assume homogeneous streams and employ static architectures with indiscriminate knowledge fusion, limiting generalizability in complex dynamic environments. To tackle this gap, we propose CAMEL, a dynamic \textbf{C}ollaborative \textbf{A}ssistance \textbf{M}ixture of \textbf{E}xperts \textbf{L}earning framework. It addresses heterogeneity by assigning each stream an independent system with a dedicated feature extractor and task-specific head. Meanwhile, a dynamic pool of specialized private experts captures stream-specific idiosyncratic patterns. Crucially, collaboration across these heterogeneous streams is enabled by a dedicated assistance expert. This expert employs a multi-head attention mechanism to distill and integrate relevant context autonomously from all other concurrent streams. It facilitates targeted knowledge transfer while inherently mitigating negative transfer from irrelevant sources. Furthermore, we propose an Autonomous Expert Tuner (AET) strategy, which dynamically manages expert lifecycles in response to drift. It instantiates new experts for emerging concepts (freezing prior ones to prevent catastrophic forgetting) and prunes obsolete ones. This expert-level plasticity provides a robust and efficient mechanism for online model capacity adaptation. Extensive experiments demonstrate CAMEL's superior generalizability across diverse multistreams and exceptional resilience against complex concept drifts.

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多数据流学习 动态学习框架 概念漂移 专家学习 模型泛化
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