ArticleAnimals : an open access journal from MDPI2022
FedAAR: A Novel Federated Learning Framework for Animal Activity Recognition with Wearable Sensors.
Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments.Scientific reports · 2025Pooled it
- Integrating Physiological Biomarkers into Precision Livestock Farming for Laying Hens: Methodological Challenges and Future Research Directions.Animals : an open access journal from MDPI · 2026Review
- Cross-Species Behavioral Representation Learning Using Domain-Adversarial Adaptation on Wearable IMU Signals.Biomimetics (Basel, Switzerland) · 2026Article
- Smart Livestock: A Federated Learning Based System for Real-Time Cattle Health, Stress and Water Resource Monitoring.Veterinary medicine and science · 2026Article
- ASAS-NANP Symposium: Mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities.Journal of animal science · 2026Review
- Advancing animal behavior recognition with self-supervised pre-training on unlabeled data.Scientific reports · 2025Article
- The Role of Artificial Intelligence in Herpesvirus Detection, Transmission, and Predictive Modeling: With a Special Focus on Marek's Disease Virus.Pathogens (Basel, Switzerland) · 2025Review
- Federated Multi-Label Learning (FMLL): Innovative Method for Classification Tasks in Animal Science.Animals : an open access journal from MDPI · 2024Article
- Rumination Detection in Sheep: A Systematic Review of Sensor-Based Approaches.Animals : an open access journal from MDPI · 2023Review
- A Review of Federated Learning in Agriculture.Sensors (Basel, Switzerland) · 2023Review
- Unsupervised Domain Adaptation for Mitigating Sensor Variability and Interspecies Heterogeneity in Animal Activity Recognition.Animals : an open access journal from MDPI · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning dominates automated animal activity recognition (AAR) tasks due to high performance on large-scale datasets. However, constructing centralised data across diverse farms raises data privacy issues. Federated learning (FL) provides a distributed learning solution to train a shared model by coordinating multiple farms (clients) without sharing their private data, whereas directly applying FL to AAR tasks often faces two challenges: client-drift during local training and local gradient conflicts during global aggregation. In this study, we develop a novel FL framework called FedAAR to achieve AAR with wearable sensors. Specifically, we devise a prototype-guided local update module to alleviate the client-drift issue, which introduces a global prototype as shared knowledge to force clients to learn consistent features. To reduce gradient conflicts between clients, we design a gradient-refinement-based aggregation module to eliminate conflicting components between local gradients during global aggregation, thereby improving agreement between clients. Experiments are conducted on a public dataset to verify FedAAR's effectiveness, which consists of 87,621 two-second accelerometer and gyroscope data. The results demonstrate that FedAAR outperforms the state-of-the-art, on precision (75.23%), recall (75.17%), F1-score (74.70%), and accuracy (88.88%), respectively. The ablation experiments show FedAAR's robustness against various factors (i.e., data sizes, communication frequency, and client numbers).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.