Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology

Swarm Learning (SL), a decentralized alternative to Federated Learning, offers privacy-preserving distributed training, but its reliance on blockchain technology hinders accessibility and scalability. This paper introduces a Simplified Peer-to-Peer Swarm Learning (P2P-SL) Frameworktailored for resource-constrained environments. By eliminating blockchain dependencies and adopting lightweight peer-to-peer communication, the proposed framework ensures robust model synchronization while maintaining data privacy. Applied to cancer histopathol-ogy, the framework integrates optimized pre-trained models, such as TorchXRayVision, enhanced with DenseNet decoders, to improve diagnostic accuracy. Extensive experiments demonstrate the framework's efficacy in handling imbalanced and biased datasets, achieving comparable performance to centralized models while preserving privacy. This study paves the way for democratizing advanced machine learning in healthcare, offering a scalable, accessible, and efficient solution for privacy-sensitive diagnostic applications. Keywords: Single-cell Sequencing Integration Multi-Omics Dimensionality Reduction Normalization. 1 Introduction The exponential growth in healthcare data, coupled with advancements in machine learning, has catalyzed significant progress in medical diagnostics [2,5,8]. However, challenges such as data privacy, imbalanced datasets, and the lack of interoperable frameworks continue to hinder the effective adoption of artificial arXiv:2504.16732v1