This research demonstrates the potential of federated learning (FL) technology for food fraud prediction using Bayesian Networks (BNs). A framework with three geographically dispersed data stations was created, each hosting different food fraud datasets. A BN algorithm was trained on these stations without data leaving its origin, preserving privacy. The study shows the applicability of federated BNs in food fraud prediction, enabling better decision-making while maintaining data confidentiality.
Publisher
npj Science of Food
Published On
Sep 01, 2023
Authors
Anand Gavai, Yamine Bouzembrak, Wenjuan Mu, Frank Martin, Rajaram Kaliyaperumal, Johan van Soest, Ananya Choudhury, Jaap Heringa, Andre Dekker, Hans J. P. Marvin
Tags
federated learning
food fraud
Bayesian Networks
privacy
data confidentiality
decision-making
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