arXiv (OAI Expanded)
open access
Decentralized Gossip Learning and Federated Averaging for Histopathology Image Classification
Breast histopathology analysis increasingly relies on distributed learning because direct data pooling across institutions is often restricted by privacy, governance, and communication constraints. This study compares server-based Federated Averaging (FedAvg), fully decentralized gossip learning, an
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Accelerating Sharded Data Parallelism at Scale with Federated Learning#1034123
- Application of Deep-learning and Ultrasound Elastography in Opportunistic Screening of Breast Cancer#827295
- Faithful Faithfulness Evaluations: Challenges & Pitfalls Learned from a Breast MRI Case Study#1061883
- L2GSSL: Label-Efficient Local-to-Global Self-Supervised Learning for Volumetric Healthcare Data#1356692
- MIRAI-MRI: Comparing Screening MRI for Patients at High Risk for Breast Cancer Identified by Mirai and Tyrer-Cuzick#853414
Record · ID 1306355
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive