← Back to all news
arXiv AI By Santhosh Parampottupadam, Melih Co\c{s}\u{g}un, Sarthak Pati, Maximilian Zenk, Saikat Roy, Dimitrios Bounias, Benjamin Hamm, Sinem Sav, Ralf Floca, Klaus Maier-Hein

Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI

Read the original on arXiv AI →

arXiv:2505. 22108v4 Announce Type: replace-cross Abstract: Background: Federated learning (FL) enables collaborative training of clinical AI models without centralizing patient data, but adoption is limited by privacy concerns, heterogeneous institutional compliance, and resource disparities; standard differential privacy (DP) applies uniform noise to all clients, penalizing well-compliant or under-resourced institutions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Sep 4

Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems

The paper introduces DP‑BR‑FedAvg, a federated learning framework that combines Gaussian‑mechanism differential privacy with a coordinate‑wise trimmed‑mean Byzantine‑robust aggregation rule. It is evaluated on a simulated cross‑institutional classification task involving fraud and clinical‑risk scoring, where it improves the F1‑score for a minority class from 0.030 (plain FedAvg) to 0.119 while bounding privacy loss. The study demonstrates that privacy and robustness mechanisms interact, and that system design for regulated, adversarial, cross‑institutional settings must account for this interaction.

By Srikumar Nayak
safety
More like this →
Hugging Face Trending Papers
Sep 2

Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems

The paper introduces DP‑BR‑FedAvg, a federated learning framework that combines Gaussian‑mechanism differential privacy with a coordinate‑wise trimmed‑mean Byzantine‑robust aggregation rule. It is evaluated on a simulated cross‑institutional classification task for fraud and clinical‑risk scoring, showing that plain FedAvg fails when a quarter of twenty clients are Byzantine, while DP‑BR‑FedAvg recovers more signal and bounds privacy loss. The study demonstrates that privacy and robustness interact, and system design for regulated, adversarial, cross‑institutional settings must account for this interaction.

safety
More like this →
arXiv AI
Jul 23

Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets

arXiv:2607. 19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings.

By Rodrigo Tertulino, Laercio Alencar, Ricardo Almeida
benchmarkssafety
More like this →
arXiv Machine Learning
Aug 20

Topology-Aware Differential Privacy in Hierarchical Federated Learning

The paper introduces Fulcrum, a topology‑aware differential privacy scheme for hierarchical federated learning that allocates noise based on the size and exposure of regional aggregation groups. By deriving a closed‑form exposure dispersion metric from region structure and weights, the method optimally balances privacy and utility, achieving up to 14.84% accuracy gains on image tasks and 12.16% on text tasks at ε = 0.99 compared to uniform noise allocation. The approach ensures each participant receives noise commensurate with its actual exposure, eliminating unnecessary privacy overhead.

By Murtaza Rangwala, Richard O. Sinnott, Rajkumar Buyya
nlpsafety
More like this →