Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data

2025-03-05

AI Summary

Federated learning faces inherent challenges in data silos, including difficulty in enhancing local knowledge, poor fairness, high communication overhead, and low reproducibility. Method: This paper proposes Knowledge-Aware Federated (KAF), a paradigm shift from global model optimization to systematic local knowledge enhancement. KAF introduces three methodological pillars—knowledge expansion, knowledge filtering, and label/feature-space calibration—integrating knowledge distillation, causal inference, subspace alignment, and uncertainty-driven active selection to handle heterogeneous, low-quality, and weakly labeled distributed data. Contribution/Results: We formally define the KAF framework and its prototype optimization objective, delineate three core technical pathways, and identify key open problems. KAF provides a principled foundation for evolving federated learning toward knowledge-empowered collaborative learning, advancing fairness, efficiency, and reproducibility while preserving data privacy and heterogeneity.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Local Search
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• 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

Abstract

Data, as an observable form of knowledge, has become one of the most important factors of production for the development of Artificial Intelligence (AI). Meanwhile, increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the ``data islands''. Although some collaborative learning paradigms such as Federated Learning (FL) can enable privacy-preserving training over decentralized data, they have inherent deficiencies in fairness, costs and reproducibility because of being learning-centric, which greatly limits the way how participants cooperate with each other. In light of this, we present a knowledge-centric paradigm termed emph{Knowledge Augmentation in Federation} (KAF), with focus on how to enhance local knowledge through collaborative effort. We provide the suggested system architecture, formulate the prototypical optimization objective, and review emerging studies that employ methodologies suitable for KAF. On our roadmap, with a three-way categorization we describe the methods for knowledge expansion, knowledge filtering, and label and feature space correction in the federation. Further, we highlight several challenges and open questions that deserve more attention from the community. With our investigation, we intend to offer new insights for what collaborative learning can bring back to decentralized data.

Problem

• Addresses limitations of Federated Learning in fairness and reproducibility.
• Proposes Knowledge Augmentation in Federation to enhance local knowledge.
• Explores methods for knowledge expansion, filtering, and space correction.

Innovation

• Knowledge-centric paradigm for decentralized data
• System architecture for knowledge augmentation
• Methods for knowledge expansion and filtering