FedDAPL: Toward Client-Private Generalization in Federated Learning

• URL: http://arxiv.org/abs/2509.23688v1
• Date: Sun, 28 Sep 2025 07:01:14 GMT
• Title: FedDAPL: Toward Client-Private Generalization in Federated Learning
• Abstract summary: Federated Learning (FL) trains models locally at each research center or clinic and aggregates only model updates.<n>A major obstacle is scanner-induced domain shift: non-biological variations in hardware or acquisition protocols can cause models to fail on external sites.<n>This paper meets these difficulties with a straightforward integration of a Domain-Adversarial Neural Network (DANN) within the FL process.
• Score: 14.449504643780834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) trains models locally at each research center or clinic and aggregates only model updates, making it a natural fit for medical imaging, where strict privacy laws forbid raw data sharing. A major obstacle is scanner-induced domain shift: non-biological variations in hardware or acquisition protocols can cause models to fail on external sites. Most harmonization methods correct this shift by directly comparing data across sites, conflicting with FL's privacy constraints. Domain Generalization (DG) offers a privacy-friendly alternative - learning site-invariant representations without sharing raw data - but standard DG pipelines still assume centralized access to multi-site data, again violating FL's guarantees. This paper meets these difficulties with a straightforward integration of a Domain-Adversarial Neural Network (DANN) within the FL process. After demonstrating that a naive federated DANN fails to converge, we propose a proximal regularization method that stabilizes adversarial training among clients. Experiments on T1-weighted 3-D brain MRIs from the OpenBHB dataset, performing brain-age prediction on participants aged 6-64 y (mean 22+/-6 y; 45 percent male) in training and 6-79 y (mean 19+/-13 y; 55 percent male) in validation, show that training on 15 sites and testing on 19 unseen sites yields superior cross-site generalization over FedAvg and ERM while preserving data privacy.

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