Robust Learning Protocol for Federated Tumor Segmentation Challenge

• Main Authors: Rawat, Ambrish, Zizzo, Giulio, Kadhe, Swanand, Epperlein, Jonathan P, Braghin, Stefano
• Format: Journal Article
• Language: English
• Published: 16-12-2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning
• DOI: 10.48550/arxiv.2212.08290

In this work, we devise robust and efficient learning protocols for
orchestrating a Federated Learning (FL) process for the Federated Tumor
Segmentation Challenge (FeTS 2022). Enabling FL for FeTS setup is challenging
mainly due to data heterogeneity among collaborators and communication cost of
training. To tackle these challenges, we propose Robust Learning Protocol
(RoLePRO) which is a combination of server-side adaptive optimisation (e.g.,
server-side Adam) and judicious parameter (weights) aggregation schemes (e.g.,
adaptive weighted aggregation). RoLePRO takes a two-phase approach, where the
first phase consists of vanilla Federated Averaging, while the second phase
consists of a judicious aggregation scheme that uses a sophisticated
reweighting, all in the presence of an adaptive optimisation algorithm at the
server. We draw insights from extensive experimentation to tune learning rates
for the two phases.