FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation
Journal:
arXiv
Published Date:
Mar 20, 2025
Abstract
Gastric cancer is one of the most commonly diagnosed cancers and has a high
mortality rate. Due to limited medical resources, developing machine learning
models for gastric cancer recognition provides an efficient solution for
medical institutions. However, such models typically require large sample sizes
for training and testing, which can challenge patient privacy. Federated
learning offers an effective alternative by enabling model training across
multiple institutions without sharing sensitive patient data. This paper
addresses the limited sample size of publicly available gastric cancer data
with a modified data processing method. This paper introduces FedSAF, a novel
federated learning algorithm designed to improve the performance of existing
methods, particularly in non-independent and identically distributed (non-IID)
data scenarios. FedSAF incorporates attention-based message passing and the
Fisher Information Matrix to enhance model accuracy, while a model splitting
function reduces computation and transmission costs. Hyperparameter tuning and
ablation studies demonstrate the effectiveness of this new algorithm, showing
improvements in test accuracy on gastric cancer datasets, with FedSAF
outperforming existing federated learning methods like FedAMP, FedAvg, and
FedProx. The framework's robustness and generalization ability were further
validated across additional datasets (SEED, BOT, FashionMNIST, and CIFAR-10),
achieving high performance in diverse environments.
