Cluster-Fair-FL for Multi-Label Consumer Health Privacy-Preserving Image Analysis

The integration of Internet of Things (IoT)-enabled consumer medical devices, platform and professional expertise has significantly improved clinical diagnosis accuracy. However, privacy concerns and variations in consumer device-specific or hospital-specific data analyses hinder centralized processing of massive multi-label medical image data. This paper proposes a Cluster-Fair Federated Learning (CFFL) framework to address data heterogeneity in distributed IoT-based consumer healthcare systems. Using an end-to-end Dual Decoder Network (DDN), medical consumers are grouped into clusters by data similarity, with cluster-specific models optimized locally and aggregated through intra-cluster volume-weighted and inter-cluster contribution-based strategies. Experiments on the Chest X-ray14 and CheXpert datasets show that the proposed CFFL improves diagnostic accuracy by 6.2% on average compared to baselines and achieves performance close to centralized learning while ensuring privacy. These results underscore the framework potential for secure, efficient, and scalable distributed consumer medical image analysis.