Query-based Knowledge Transfer for Heterogeneous Learning Environments

2025-04-12

AI Summary

To address the challenge of knowledge adaptation under data heterogeneity and privacy constraints—where client-side feature representations are impoverished due to the absence of raw data sharing—this paper proposes a query-driven, decentralized knowledge transfer framework that requires no raw data exchange. Its core contributions are threefold: (1) a data-agnostic masked query mechanism for task-oriented knowledge refinement; (2) query-focused knowledge distillation coupled with dynamic, task-specific parameter updating to mitigate knowledge interference and catastrophic forgetting; and (3) a lightweight decentralized collaborative architecture that substantially reduces communication overhead. Evaluated on standard and clinical benchmarks, the framework achieves absolute improvements of 20.91% and 14.32% in single-class and multi-class query accuracy, respectively—outperforming state-of-the-art federated learning, ensemble learning, and transfer learning approaches.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Distributed Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Enables tailored knowledge transfer without data exchange
• Addresses client needs in heterogeneous decentralized learning
• Reduces knowledge interference and forgetting in collaborative settings

Innovation

• Query-based Knowledge Transfer for tailored learning
• Data-free masking for efficient knowledge transfer
• Refines task-specific parameters to reduce interference