FedQuadContra: Ambiguity-Aware Contrapositive Quadruplet Learning for Federated Remote Sensing Scene Classification

Federated learning enables collaborative model training across decentralized data while preserving data locality, yet its performance can degrade substantially under statistical heterogeneity, where skewed local class distributions induce biased representations and inconsistent local optimization. This challenge is particularly pronounced in remote sensing scene classification, where high inter-class similarity and substantial intra-class variability further complicate discriminative representation learning. We introduce FedQuadContra, an ambiguity-aware contrapositive quadruplet learning framework for federated representation learning under heterogeneous client distributions. Unlike conventional pairwise and triplet metric-learning objectives that capture a limited set of inter-sample relationships, FedQuadContra constructs each quadruplet using an anchor, a same-class positive, a hard negative, and an ambiguity-aware contrapositive negative selected from a semantic class distinct from both the anchor and hard-negative classes. This construction extends metric learning beyond a single competing negative class by explicitly modeling multiple inter-class relationships within each quadruplet. The resulting multi-relational objective jointly promotes intra-class compactness, hard inter-class separation, and discrimination among representation-wise confusable samples, enabling more discriminative representation learning under heterogeneous client distributions. We evaluate FedQuadContra under varying degrees of statistical heterogeneity on remote sensing scene classification benchmarks and across multiple network architectures. Extensive experiments show that FedQuadContra consistently outperforms representative federated learning and federated contrastive-learning methods, including the recent RS-CCL approach, across the evaluated data distributions, with particularly pronounced improvements under severe heterogeneity. These results demonstrate the effectiveness of ambiguity-aware multi-relational representation learning for federated remote sensing scene classification.