Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation
Journal:
arXiv
Published Date:
Jan 6, 2025
Abstract
Cardiovascular disease (CVD) and cardiac dyssynchrony are major public health
problems in the United States. Precise cardiac image segmentation is crucial
for extracting quantitative measures that help categorize cardiac dyssynchrony.
However, achieving high accuracy often depends on centralizing large datasets
from different hospitals, which can be challenging due to privacy concerns. To
solve this problem, Federated Learning (FL) is proposed to enable decentralized
model training on such data without exchanging sensitive information. However,
bandwidth limitations and data heterogeneity remain as significant challenges
in conventional FL algorithms. In this paper, we propose a novel efficient and
adaptive federate learning method for cardiac segmentation that improves model
performance while reducing the bandwidth requirement. Our method leverages the
low-rank adaptation (LoRA) to regularize model weight update and reduce
communication overhead. We also propose a \mymethod{} aggregation technique to
address data heterogeneity among clients. This technique adaptively penalizes
the aggregated weights from different clients by comparing the validation
accuracy in each client, allowing better generalization performance and fast
local adaptation. In-client and cross-client evaluations on public cardiac MR
datasets demonstrate the superiority of our method over other LoRA-based
federate learning approaches.
