无需交换数据，通过查询实现个性化知识迁移。
Query-based Knowledge Transfer for Heterogeneous Learning Environments
- 基于查询的掩码策略，实现高效、定向的知识传递。
- 单类查询下性能提升20.91%，多类查询下提升14.32%。
- 适合数据异构且隐私敏感的分布式学习场景。
在数据异构与隐私约束下的去中心化协同学习快速发展，但现有方法如联邦学习、集成学习和迁移学习，难以满足客户端的个性化需求，尤其当本地数据表征能力受限时。为此，我们提出一种名为查询式知识迁移（Query-based Knowledge Transfer, QKT）的新框架，可在不直接交换数据的前提下，针对特定客户端需求进行定制化知识获取。QKT采用无数据掩码策略，实现通信高效的查询聚焦型知识迁移，并通过优化任务特定参数缓解知识干扰与遗忘问题。在标准与临床基准上的实验表明，QKT在单类查询设置下平均性能优于现有方法20.91个百分点，在多类查询场景下平均提升14.32个百分点。进一步分析与消融实验显示，QKT能有效平衡新旧知识的学习，展现出在去中心化学习中的巨大应用潜力。
原文摘要 · Abstract (English)
Decentralized collaborative learning under data heterogeneity and privacy constraints has rapidly advanced. However, existing solutions like federated learning, ensembles, and transfer learning, often fail to adequately serve the unique needs of clients, especially when local data representation is limited. To address this issue, we propose a novel framework called Query-based Knowledge Transfer (QKT) that enables tailored knowledge acquisition to fulfill specific client needs without direct data exchange. QKT employs a data-free masking strategy to facilitate communication-efficient query-focused knowledge transfer while refining task-specific parameters to mitigate knowledge interference and forgetting. Our experiments, conducted on both standard and clinical benchmarks, show that QKT significantly outperforms existing collaborative learning methods by an average of 20.91\% points in single-class query settings and an average of 14.32\% points in multi-class query scenarios. Further analysis and ablation studies reveal that QKT effectively balances the learning of new and existing knowledge, showing strong potential for its application in decentralized learning.
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