# Federated Learning for Clients' Data Privacy Assurance in Food Service Industry
## 5 Results and Discussion
### Privacy and Performance Trade-Off
Figure 7: Privacy performance trade-off for the classification task. several areas [29; 49]. We believe this is the first study where the main focus is on using FL in the food service industry, or for identification and segmentation of contamination. However, implementing federated learning in the food service industry is not without its challenges. The deployment of FL requires a robust and reliable infrastructure that supports secure and efficient data communication. A secure and reliable communication channel is vital to maintain the integrity and privacy of the data during the exchange of updated model parameters between the central server and client devices. Further, the client devices must possess adequate computational capabilities to locally train complex deep learning models. This might be challenging, given that most devices used in the food service industry may not be equipped with high-end computational resources. Another hurdle is dealing with the data heterogeneity intrinsic to FL due to diverse, decentralized data sources. This heterogeneity could influence the model's performance, necessitating the development of techniques to mitigate its effects. Lastly, coordinating the training process among numerous client devices, ensuring synchronization, handling device failures or dropouts, and integrating new clients into the learning process all require robust protocols. The main purpose of this research is a proof of concept for using cutting-edge imaging technology together with state-of-the-art deep learning algorithms and federated learning to enhance cleanliness assurance in the food service industry, while assuring client data remains private and within the client's information technology domain. We have shown that deep learning-based models trained using the FL approach could achieve results comparable to those of the centralized methods. In this particular case, training models using the FL approach can rapidly detect contamination using a portable handheld scanning device, paving the way for more precise, rapid, reliable, and secure examinations. This demonstrates how FL can be applied in other sensing and classification technologies. There are trade-offs between privacy and performance, and it is important to consider the need for training classification algorithms for deployment on devices with more limited processing capabilities. While centralized models can give the best performance, based on our experience discussing food safety issues with the industry, it is unrealistic to expect that clients would be willing to share any data that could negatively reflect on their sanitization procedures and potentially impact business value [50; 51]. Our comparison of the benefit of FL vs. a theoretical scenario where all clients would be willing to share all their data is intended to illustrate what might be achievable with sufficient participation. While the magnitude of improvement that we discuss here seems relatively small, the benefit can be significant. An increase in accuracy means a decrease in false positives and false negatives. If we can reduce false positives and false negatives from 5% to 2%, that is a 60% reduction in contamination on food preparation or food production surfaces that can host pathogenic organisms. While false positives have a small impact in that they require additional cleaning, false negatives can result in contamination of entire batches of food, which can result in product recalls, illness, or death of customers and attendant legal consequences, as well as major damage to brand equity [48]. Thus, there is quite a benefit to a customer that participates in improving the model if privacy can be maintained. Other privacy preserving schemes such as data anonymization, differential privacy, or cryptographic methods [52; 53; 54] all involve some transfer of data outside of an organization, so there is potential for reconstruction of original data. There is also some loss in the utility of models generated by this data.