Hybrid Gossip-FedAvg shows promise in decentralized histopathology image classification

Researchers have compared three distributed learning methods for histopathology image classification: server-based Federated Averaging (FedAvg), decentralized gossip learning, and a hybrid approach combining both. Using a dataset of over 277,000 image patches for invasive ductal carcinoma classification, the study found that Hybrid Gossip-FedAvg achieved a competitive ROC-AUC of 0.8811, closely followed by FedAvg. Across multiple patient-level repetitions, FedAvg and Hybrid Gossip-FedAvg demonstrated similar performance in mean ROC-AUC, with Hybrid showing a higher mean area under the precision-recall curve. The findings suggest that while FedAvg offers a reliable baseline, topology-aware gossip methods provide a viable decentralized alternative, and the hybrid approach balances peer-to-peer diffusion with global coordination. AI

IMPACT This research explores decentralized learning methods for medical image analysis, potentially enabling broader collaboration in sensitive data environments.

RANK_REASON Academic paper detailing a new method for distributed learning in image classification. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
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• DagsHub
• FedAvg
• Gotit.pub
• histopathology image classification
• Hugging Face
• Hybrid Gossip-FedAvg
• invasive ductal carcinoma
• ROC-AUC
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