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arXiv · 2601.12664

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

Elisa Gonçalves Ribeiro·Rodrigo Moreira·Larissa Ferreira Rodrigues Moreira·André Ricardo Backes
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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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Elisa Gonçalves Ribeiro, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, André Ricardo Backes. 2026-01-19. Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images. https://arxiv.org/abs/2601.12664

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