cs.CVApr 1, 2026

FecalFed: Privacy-Preserving Poultry Disease Detection via Federated Learning

Authors: Tien-Yu Chi

Organizations: Independent Researcher

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

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 FecalFed\textbf{FecalFed}, a privacy-preserving federated learning framework for poultry disease classification. We first curate and release poultry-fecal-fl\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 α=0.5α=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.

Figures & tables

Figure 1: Left: The FecalFed cross-silo federated learning architecture. Raw images remain isolated on-farm, while only Swin-Tiny model weights are communicated to the central server for FedAdam aggregation. Right: Test accuracy across the 10 isolated farm partitions (gray) demonstrating extreme variance and collapse under non-IID conditions, compared to the stable performance recovery achieved by FecalFed (orange).
Figure 2: The FecalFed data curation pipeline. (Top) The dual-hash deduplication process eliminated 46.89% of the raw aggregated dataset. (Bottom) A visual example of severe cross-source contamination, where original field data was found repeatedly down-sampled and duplicated in synthetic open-source repositories.
Figure 3: Data distribution across 10 isolated farm clients under a Dirichlet ( α=0.5\alpha=0.5 ) non-IID partitioning strategy. This extreme skew mimics realistic agricultural settings where localized outbreaks cause specific edge devices to hold a vast majority of samples for a single disease.
Model ArchitectureParametersSingle-Farm (Non-IID)Centralized (Upper Bound)FL (FedAvg)FL (FedAdam)Gap (Best FL vs. Cent.)
Swin-Small50M64.86% ±\pm 24.95%95.10%89.74%90.31%-4.79%
ViT-B/1686M60.97% ±\pm 23.05%94.81%88.77%90.02%-4.79%
Swin-Tiny28M64.03% ±\pm 24.04%93.04%86.89%89.74%-3.30%
ViT-S/1622M65.87% ±\pm 22.06%92.99%89.28%85.12%-3.71%
Table 1: Performance comparison of vision foundation models across training paradigms under non-IID conditions (Dirichlet α=0.5\alpha=0.5 ). The baseline for isolated single-farm training collapses to the 60%60\% – 65%65\% range across all architectures due to extreme local data skew. Federated learning successfully recovers this lost performance, with FedAdam providing the highest peak accuracy for larger models and minimizing the gap to the centralized upper bound.
Figure 4: Federated test accuracy versus model parameter footprint. The Swin-Tiny architecture (28M parameters) emerges as the optimal candidate for edge deployment, achieving highly competitive accuracy (89.74%) while drastically reducing the computational and memory requirements compared to larger models like ViT-B/16.
Figure 5: Ablation study on global communication rounds using the ViT-B/16 baseline under FedAvg. The top panel shows test accuracy convergence, while the bottom panel shows test loss decay. Extending the training duration to 20 rounds continuously mitigates the effects of non-IID data skew, yielding smooth, stable performance improvements without stalling.
More in this topic

Classification

Classification maps observations to discrete labels, including familiar, unknown, hierarchical, or multiple categories.

See all 401 papers

Explore similar work

CardsList
Sep 1, 2026

Private Computation Space: Experience with Trusted Multi-Cluster Federated Learning for Agriculture

Artificial Intelligence has shown to help improve agricultural practices, yet adoption remains limited: 69% of U.S. farmers have privacy concerns with sharing their data, and these concerns must be addressed before adoption is widespread. While Federated Learning has been demonstrated to protect privacy at scale for other sectors, deploying a system for agriculture comes with its own set of challenges; the problem necessitates a system that can protect farmer data and identities while preserving model utility, runs on commodity hardware, and is resilient to fragile rural infrastructure. To address these concerns, we introduce the Private Computation Space (PCS), a deployed, open-source Machine Learning system to provision and process farmer data securely. We design a system tailored to an agricultural setting, with multi-cluster orchestration for reliability in rural areas with asynchronous Federated Learning (FL), Differential Privacy (DP), and Trusted Execution Environments (TEEs), to allow farms to participate in the framework while keeping their data private. We evaluate the system on two deployed workloads: monitoring nitrogen with living plant sensors in NY for six months and predicting evapotranspiration from weather stations in CA for ten months. Our evaluation finds a Dice Similarity Coefficient (DSC) of 0.71 and R2R^2 accuracy of 0.84 for the respective workloads, improving the worst single-site model accuracy by 22.4% and 9.1%, respectively, while preserving privacy.

Shuangyu Lei, Muhammad Salman Abid, Jacob Belding +17

Cornell University USA

Privacy-Preserving Federated LearningFederated LearningClustered Federated LearningPrecision AgricultureDifferentially Private Federated Learning
AskPDF
Feb 18, 2026

U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition

Federated learning has emerged as a privacy-preserving and efficient approach for deploying intelligent agricultural solutions. Accurate edge-based diagnosis across geographically dispersed farms is crucial for recognising tomato diseases in sustainable farming. Traditional centralised training aggregates raw data on a central server, leading to communication overhead, privacy risks and latency. Meanwhile, edge devices require lightweight networks to operate effectively within limited resources. In this paper, we propose U-FedTomAtt, an ultra-lightweight federated learning framework with attention for tomato disease recognition in resource-constrained and distributed environments. The model comprises only 245.34K parameters and 71.41 MFLOPS. First, we propose an ultra-lightweight neural network with dilated bottleneck (DBNeck) modules and a linear transformer to minimise computational and memory overhead. To mitigate potential accuracy loss, a novel local-global residual attention (LoGRA) module is incorporated. Second, we propose the federated dual adaptive weight aggregation (FedDAWA) algorithm that enhances global model accuracy. Third, our framework is validated using three benchmark datasets for tomato diseases under simulated federated settings. Experimental results show that the proposed method achieves 0.9910% and 0.9915% Top-1 accuracy and 0.9923% and 0.9897% F1-scores on SLIF-Tomato and PlantVillage tomato datasets, respectively.

Romiyal George, Sathiyamohan Nishankar, Selvarajah Thuseethan +4

Department of Computer Engineering, University of Peradeniya, Sri Lanka. · Faculty of Science and Technology, Charles Darwin University, Australia. · Canadian Institute for Cybersecurity, University of New Brunswick, Canada. +1

Lightweight Neural NetworksFederated LearningPlant Disease ClassificationFederated Learning AggregationEdge MLAttention Mechanisms
AskPDF
Jun 10, 2026

Fed-FBD: Federated Functional Block Diversification for Isolation, Privacy, and Surgical Unlearning

Federated learning (FL) enables collaborative model training without sharing raw patient data, but standard approaches such as FedAvg treat each client as a black box and provide no mechanism for isolating an adversarial contributor, auditing per-client influence, or honoring a departed participant's right to be forgotten. We present Fed-FBD (Federated Functional Block Diversification), a modular federated architecture that decomposes a ResNet backbone into six functional blocks (the stem, four residual groups, and the classification head) and maintains a warehouse of N color variants, each assembled from independently tracked and contributor-stamped blocks. Fed-FBD provides three capabilities absent in FedAvg: (i) architecturally guaranteed block-level isolation, so that an adversarial or mislabelled client cannot contaminate the clean colous; (ii) privacy-by-design, where membership inference advantage is already indistinguishable from chance before any privacy mechanism is applied; and (iii) surgical machine unlearning of a departed participant's contribution at sub-second cost and without retraining. Experiments on six MedMNIST-2D datasets, PathMNIST at 224x224, and CIFAR-10 show that Fed-FBD trades a modest 0.3%-3.1% IID accuracy gap on the adequately sized datasets for these guarantees, remains within 0.8%-4.0% of FedAvg at Dirichlet alpha=1.0 on three of four datasets, and confines all six adversarial attacks we study to the poisoned client's own blocks with at most +/-0.01 AUC drift on the clean colors.

Weijie Chen, Alan B. McMillan

Department of Radiology, University of Wisconsin–Madison

Federated UnlearningByzantine-Robust Federated LearningPrivacy-Preserving MLFederated LearningPrivacy-Preserving Federated Learning
AskPDF