# Federated Learning for Clients' Data Privacy Assurance in Food Service Industry
## 1 Introduction
Centralized data collection may create privacy issues for people and liability problems for corporations if data is not handled correctly. Since people are increasingly concerned about data privacy and security, governments and organizations have begun enforcing legislation to safeguard personal information [17; 18; 19]. Since the most frequent sites of foodborne illness outbreaks are restaurants and institutional kitchens, sanitization and infection prevention in these facilities are vital. New technologies such as the SafetySpect sanitization inspection device [20] can help restaurants and institutional kitchens keep their environment safer and improve their level of cleanliness. However, companies and institutions are concerned that using new technologies can increase privacy risks, and leaks of sensitive information (e.g., the presence of contamination in the kitchen or dining area) could jeopardize their reputation. There is a need to convince such organizations and facilities that their sensitive information and data will be safe and secure when participating in shared-data projects. New avenues of ML research have opened up with the advent of federated learning (FL) [21]. FL allows ML algorithms to learn from data without transferring data to a centralized server. FL lets the training data remain distributed on multiple clients' devices while the ML models are downloaded and the models trained on the decentralized local data. The updated model parameters are then returned to the centralized server without transferring any of the client's raw data. FL allows several clients to work together to train an ML model without sharing any private data or sensitive information. In order to train the FL model, it is necessary to have a coordinating agent that is responsible for handling the information exchange [22]. FL spreads the computational power requirements across all clients while preserving client privacy and eliminating the cost of data transfer and storage needed with centralized processing. Each client's computational power is used to analyze their own data. The application of FL has recently gained attention in various \"edge\" device applications, such as in mobile devices for text prediction on virtual keyboards [23], improving the quality of search suggestions [24], and emoji suggestions based on the text typed on a keyboard [25]. Another notable application of FL is in the medical field, where data typically includes private and sensitive patient information that cannot be shared outside of an organization, limiting its use in public research. For example, in [26], the authors proposed a framework to investigate changes in the subcortical brain caused by neurological disorders. In [27], a heterogeneous FL method was proposed for training ML models for electroencephalography data classification, and the effectiveness of FL for the identification of COVID-19 using chest X-ray images was investigated in [28]. FL has also been investigated in many other areas [29], but to our knowledge, there are no publications focusing on using FL in food safety and cleanliness assessments in the food service industry. In this study, we use a fluorescence-imaging technology combined with an FL-based deep learning model for the detection of contamination and image segmentation of contamination in fluorescence images of a variety of food-handling surfaces. Combining FL-based deep learning models with fluorescence imaging for contamination detection could improve the level of safety and cleanliness in institutional kitchens and restaurants while assuring safety and privacy of client data. In this paper, the data collection technology and process are explained in Section 2. The FL framework and deep learning algorithms used for the classification and segmentation of contamination are described in Section 3. The results and performance evaluation of this study are shown in Section 4. Section 5 concludes with the main achievements of this study.