Siloed Federated Learning for Multi-Centric Histopathology Datasets
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Abstract (arXiv)
While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially when applied in the medical domain where multi-centric data heterogeneity is common. Building on previous domain adaptation works, this paper proposes a novel federated learning approach for deep learning architectures via the introduction of local-statistic batch normalization (BN) layers, resulting in collaboratively-trained, yet center-specific models. This strategy improves robustness to data heterogeneity while also reducing the potential for information leaks by not sharing the center-specific layer activation statistics. We benchmark the proposed method on the classification of tumorous histopathology image patches extracted from the Camelyon16 and Camelyon17 datasets. We show that our approach compares favorably to previous state-of-the-art methods, especially for transfer learning across datasets.
Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Aug 2020.
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Citing papers per year: 2021: 15; 2022: 19; 2023: 15; 2024: 7; 2025: 1; 2026: 4.
61 papers.
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50 most cited of 61 All 61 (JSON)
- 2026FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification25 Sep 2026
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- 2024Deep Domain Isolation and Sample Clustered Federated Learning for Semantic Segmentation4 Oct 2024
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- 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare15 Sep 2024
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- 2024Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities17 Jun 2024
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38 more
- 2023Recent Methodological Advances in Federated Learning for Healthcare4 Oct 2023
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- 2023FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving23 Sep 2023
- 2023GradientCoin: A Peer-to-Peer Decentralized Large Language Models21 Aug 2023
- 2023Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space26 Jul 2023
- 2023A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation14 Jun 2023
- 2023Federated Learning for Medical Image Analysis: A Survey9 Jun 2023
- 2023FedWon: Triumphing Multi-domain Federated Learning Without Normalization9 Jun 2023
- 2023Federated Domain Generalization: A Survey2 Jun 2023
- 2023Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning9 Apr 2023
- 2023Experimenting with Normalization Layers in Federated Learning on non-IID scenarios19 Mar 2023
- 2023Making Batch Normalization Great in Federated Deep Learning12 Mar 2023
- 2023FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning27 Feb 2023
- 2023Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading13 Feb 2023
- 2023Why Batch Normalization Damage Federated Learning on Non-IID Data?8 Jan 2023
- 2022FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction3 Dec 2022
- 2022Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review27 Nov 2022
- 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings10 Oct 2022
- 2022SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning4 Oct 2022
- 2022Federated Stain Normalization for Computational Pathology29 Sep 2022
- 2022Federated Learning of Large Models at the Edge via Principal Sub-Model Training28 Aug 2022
- 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions5 Aug 2022
- 2022Personalizing Federated Medical Image Segmentation via Local Calibration11 Jul 2022
- 2022Towards the Practical Utility of Federated Learning in the Medical Domain7 Jul 2022
- 2022Towards Federated Long-Tailed Learning30 Jun 2022
- 2022Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images31 May 2022
- 2022FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation30 May 2022
- 2022FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation23 May 2022
- 2022Uncertainty Minimization for Personalized Federated Semi-Supervised Learning5 May 2022
- 2022Improving Generalization in Federated Learning by Seeking Flat Minima22 Mar 2022
- 2022On the Pitfalls of Batch Normalization for End-to-End Video Learning: A Study on Surgical Workflow Analysis15 Mar 2022
- 2022FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving28 Feb 2022
- 2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?14 Feb 2022
- 2022Federated Learning Challenges and Opportunities: An Outlook1 Feb 2022
- 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images20 Dec 2021
- 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learning30 Nov 2021
- 2021FedDropoutAvg: Generalizable federated learning for histopathology image classification25 Nov 2021
- 2021Decentralized Federated Learning through Proxy Model Sharing22 Nov 2021
50 newest of 61 All 61 (JSON)
- 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
- 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
- 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
- 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
- 2022FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extractioncited by 102
- 2021Gradient Inversion with Generative Image Priorcited by 101
- 2022Improving Generalization in Federated Learning by Seeking Flat Minimacited by 98
- 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
- 2021Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Datacited by 69
- 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 62
- 2021Decentralized Federated Learning through Proxy Model Sharingcited by 56
- 2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?cited by 44
12 most cited of 61 All 61 (JSON)
- 2026FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification25 Sep 2026
- 2026Federated Learning for Feature Generalization with Convex Constraints12 Jun 2026
- 2026Personalized Federated Learning with Residual Fisher Information for Medical Image Segmentation16 Mar 2026
- 2026FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentation12 Mar 2026
- 2025Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning28 May 2025
- 2024Deep Domain Isolation and Sample Clustered Federated Learning for Semantic Segmentation4 Oct 2024
- 2024An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration2 Oct 2024
- 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare15 Sep 2024
- 2024Feature Diversification and Adaptation for Federated Domain Generalization11 Jul 2024
- 2024Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities17 Jun 2024
- 2024When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather20 Mar 2024
- 2024A Systematic Review of Generalization Research in Medical Image Classification18 Mar 2024
12 newest of 61 All 61 (JSON)