mlsanityData to 7 Oct 2026

The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector

arXiv 2104.07468 ↗First posted 14 Apr 2021cs.LG cs.CRcited by 16

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Abstract (arXiv)

Data sharing remains a major hindering factor when it comes to adopting emerging AI technologies in general, but particularly in the agri-food sector. Protectiveness of data is natural in this setting; data is a precious commodity for data owners, which if used properly can provide them with useful insights on operations and processes leading to a competitive advantage. Unfortunately, novel AI technologies often require large amounts of training data in order to perform well, something that in many scenarios is unrealistic. However, recent machine learning advances, e.g. federated learning and privacy-preserving technologies, can offer a solution to this issue via providing the infrastructure and underpinning technologies needed to use data from various sources to train models without ever sharing the raw data themselves. In this paper, we propose a technical solution based on federated learning that uses decentralized data, (i.e. data that are not exchanged or shared but remain with the owners) to develop a cross-silo machine learning model that facilitates data sharing across supply chains. We focus our data sharing proposition on improving production optimization through soybean yield prediction, and provide potential use-cases that such methods can assist in other problem settings. Our results demonstrate that our approach not only performs better than each of the models trained on an individual data source, but also that data sharing in the agri-food sector can be enabled via alternatives to data exchange, whilst also helping to adopt emerging machine learning technologies to boost productivity.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 20 of its 58 references that are in the map. First posted on arXiv 14 Apr 2021.

Builds on

20 of 58 references.

References · 20

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  7. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
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  10. 2020Federated Learning with Matched Averagingcited by 564
  11. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  12. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
8 more
  1. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  2. 2017Collecting Telemetry Data Privatelycited by 147
  3. 2020LDP-Fed: Federated Learning with Local Differential Privacycited by 129
  4. 2017Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentationcited by 58
  5. 2019A CNN-RNN Framework for Crop Yield Predictioncited by 36
  6. 2019Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environmentscited by 9
  7. 2020Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspectivecited by 7
  8. 2019Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systemscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 4; 2024: 3; 2025: 1; 2026: 4.

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  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2023Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharingcited by 7
  3. 2021Fully Homomorphically Encrypted Deep Learning as a Servicecited by 6
  4. 2023Strategic Data Sharing between Competitorscited by 6
  5. 2022Premonition Net, A Multi-Timeline Transformer Network Architecture Towards Strawberry Tabletop Yield Forecastingcited by 4
  6. 2024Incentivizing Truthful Collaboration in Heterogeneous Federated Learningcited by 3
  7. 2021EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecastingcited by 1
  8. 2022Asynchronous Collaborative Learning Across Data Siloscited by 1
  9. 2023FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flowscited by 1
  10. 2024Federated learning in food researchcited by 1
  11. 2024Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Applicationcited by 1
  12. 2026Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysiscited by 1
4 more
  1. 2025Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learningcited by 0
  2. 2026Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecastingcited by 0
  3. 2026FecalFed: Privacy-Preserving Poultry Disease Detection via Federated Learningcited by 0
  4. 2026Agent-Based Post-Hoc Correction of Agricultural Yield Forecastscited by 0