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

2025-04-23

AI Summary

To address challenges in cancer histopathological diagnosis—including sensitive medical data privacy, severe class imbalance, and poor cross-institutional interoperability—this paper proposes P2P Swarm Learning, a blockchain-free, lightweight decentralized learning framework. It operates over a pure peer-to-peer communication topology, eliminating costly blockchain dependencies. The framework integrates an optimized TorchXRayVision backbone with a DenseNet decoder and incorporates class-weighted loss and gradient clipping to mitigate data skew. Evaluated on multi-center histopathological image datasets, the model achieves an AUC ≥ 0.94—comparable to centralized training—while reducing communication overhead by 62%. It supports dynamic scaling to hundreds of nodes and demonstrates enhanced robustness and deployability in resource-constrained clinical environments.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Security and Privacy: Blockchains and distributed ledgers

Abstract

The complexities of healthcare data, including privacy concerns, imbalanced datasets, and interoperability issues, necessitate innovative machine learning solutions. 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 extit{Simplified Peer-to-Peer Swarm Learning (P2P-SL) Framework} tailored 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 histopathology, 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.

Problem

• Decentralized privacy-preserving cancer diagnosis without blockchain
• Handling imbalanced biased histopathology data efficiently
• Scalable lightweight framework for resource-constrained healthcare

Innovation

• Simplified Peer-to-Peer Swarm Learning Framework
• Lightweight communication without blockchain dependency
• Optimized pre-trained models with DenseNet decoders