FecalFed: Privacy-Preserving Poultry Disease Detection via Federated Learning

TL;DR

FecalFed introduces a federated learning framework for poultry disease detection that preserves farm data privacy, improves accuracy under data heterogeneity, and releases a deduplicated fecal image dataset.

Contribution

The paper presents a novel privacy-preserving federated learning approach for poultry disease classification and releases a deduplicated, high-quality dataset for the community.

Findings

Federated learning recovers model performance under non-IID data conditions.

Server-side adaptive optimization (FedAdam) with Swin-Small achieves 90.31% accuracy.

Edge-optimized Swin-Tiny maintains 89.74% accuracy, enabling efficient on-farm monitoring.

Abstract

Early detection of highly pathogenic avian influenza (HPAI) and endemic poultry diseases is critical for global food security. While computer vision models excel at classifying diseases from fecal imaging, deploying these systems at scale is bottlenecked by farm data privacy concerns and institutional data silos. Furthermore, existing open-source agricultural datasets frequently suffer from severe, undocumented data contamination. In this paper, we introduce $\textbf{FecalFed}$, a privacy-preserving federated learning framework for poultry disease classification. We first curate and release $\texttt{poultry-fecal-fl}$, a rigorously deduplicated dataset of 8,770 unique images across four disease classes, revealing and eliminating a 46.89$\%$ duplication rate in popular public repositories. To simulate realistic agricultural environments, we evaluate FecalFed under highly heterogeneous, non-IID conditions (Dirichlet $\alpha=0.5$). While isolated single-farm training collapses under this data heterogeneity, yielding only 64.86$\%$ accuracy, our federated approach recovers performance without centralizing sensitive data. Specifically, utilizing server-side adaptive optimization (FedAdam) with a Swin-Small architecture achieves 90.31$\%$ accuracy, closely approaching the centralized upper bound of 95.10\%. Furthermore, we demonstrate that an edge-optimized Swin-Tiny model maintains highly competitive performance at 89.74$\%$, establishing a highly efficient, privacy-first blueprint for on-farm avian disease monitoring.