PathwayFed: Disentangling Client-Specific Knowledge Pathways for Federated Multi-Label Learning

Federated Multi-Label Learning (FML) enables multiple clients to collaboratively learn from heterogeneous multi-label data while preserving data privacy. However, variations in local label spaces and label co-occurrence patterns cause different clients to encode specialized knowledge through entangled feature-to-class path- ways, resulting in implicit conflicts and the dilution of informative evidence during aggregation. To address this challenge, we propose PathwayFed, a novel frame- work that makes client-specific knowledge pathways more distinguishable and disentangles their contributions during federated aggregation. On the client side, PathwayFed refines knowledge pathways through dimensional expansion and an orthogonal sparse transformation, producing finer-grained and less-overlapping pathway expressions that make meaningful client-specific knowledge easier to identify and quantify. On the server side, we introduce a matched knowledge routing mechanism that reinforces strongly expressed client-specific knowledge while preserving its pathway association, followed by conflict-aware projection to suppress conflicting components and calibrate the compatibility of the routed knowledge pathways. Extensive experiments on PASCAL VOC 2007, MS-COCO 2014, and NUS-WIDE-81 demonstrate that PathwayFed outperforms state-of-the- art methods, particularly under severe heterogeneity.