Data-Aware Layer Assignment for Secure and Efficient Communication in Federated Learning for Medical Image Analysis
Document Type
Article
Publication Date
2025
DOI
10.1609/aaaiss.v7i1.36926
Publication Title
Proceedings of the AAAI Symposium Series
Volume
7
Issue
1
Pages
516-523
Conference Name
2025 AAAI Fall Symposium Series, November 6-8, 2025, Arlington, Virginia
Abstract
Cross-silo medical imaging federations must contend with strict privacy, limited bandwidth, and non identically distributed (non-IID) data that destabilize training. Current federated learning (FL) architectures either carry the full model (e.g., FedAvg/FedProx) or use naive client/layer pruning and random sampling while ignoring both non-IID heterogeneity and per-layer utility. Based on these limitations, the paper presents a dataaware, layer-wise protocol that aligns communication with expected loss descent while bounding per-round client leverage. Each round, the server estimates per-layer influence from a tiny root set, and clients expose lightweight metadata to form data-quality scores. A capacity-constrained entropic transport matches high-influence layers to high-quality clients under redundancy and temporal coverage. Clients train all layers but upload exactly one with train-all, send-one principle. The server then performs per-layer robust aggregation on masked updates via secure aggregation. On the three cross-silo imaging benchmarks of Pneumonia CXR, Brain-Tumor MRI, and ISIC Skin Cancer, it demonstrates a strong threshold free detection quality (AUROC/ AUPRC: 0.925/0.935, 0.996/0.988, 0.834/0.852, respectively) while also reducing the per round up-link by ≈ 1/n with respect to FedAvg (e.g., ≈ 10× with 10 clients) by only receiving one layer per client. Indicating its viability for deployment-grade secure aggregation for hospital networks.
Rights
© 2023, Association for the Advancement of Artificial Intelligence. All rights reserved.
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Original Publication Citation
Gonthina, S. S., Roy, S., Bhattacharya, P., Rana, P., & Shetty, S. (2025). Data-aware layer assignment for secure and efficient communication in federated learning for medical image analysis. Proceedings of the AAAI Symposium Series, 7(1), 516-523. https://doi.org/10.1609/aaaiss.v7i1.36926
Repository Citation
Gonthina, S. S., Roy, S., Bhattacharya, P., Rana, P., & Shetty, S. (2025). Data-aware layer assignment for secure and efficient communication in federated learning for medical image analysis. Proceedings of the AAAI Symposium Series, 7(1), 516-523. https://doi.org/10.1609/aaaiss.v7i1.36926
ORCID
0000-0001-9199-2479 (Rana), 0000-0002-8789-0610 (Shetty)