Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images

• URL: http://arxiv.org/abs/2601.12664v1
• Date: Mon, 19 Jan 2026 02:24:24 GMT
• Title: Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images
• Authors: Elisa Gonçalves Ribeiro, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, André Ricardo Backes,
• Abstract summary: Deep learning for cancer histopathology training conflicts with privacy constraints in clinical settings.<n>Deep learning for cancer histopathology training conflicts with privacy constraints in clinical settings.<n> Federated Learning (FL) mitigates this by keeping data local.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning for cancer histopathology training conflicts with privacy constraints in clinical settings. Federated Learning (FL) mitigates this by keeping data local; however, its performance depends on hyperparameter choices under non-independent and identically distributed (non-IID) client datasets. This paper examined whether hyperparameters optimized on one cancer imaging dataset generalized across non-IID federated scenarios. We considered binary histopathology tasks for ovarian and colorectal cancers. We perform centralized Bayesian hyperparameter optimization and transfer dataset-specific optima to the non-IID FL setup. The main contribution of this study is the introduction of a simple cross-dataset aggregation heuristic by combining configurations by averaging the learning rates and considering the modal optimizers and batch sizes. This combined configuration achieves a competitive classification performance.

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