# Federated Learning for Clients' Data Privacy Assurance in Food Service Industry
## 1 Introduction  
Foodborne illness contributes significantly to morbidity and mortality as a public health issue. In 2010, contaminated food caused around 600 million cases of foodborne disease and 420 thousand fatalities worldwide [1]. According to the US Centers for Disease Control and Prevention (CDC), approximately 48 million Americans are sickened each year, 128,000 are hospitalized, and 3000 are killed by foodborne illnesses [2]. According to the CDC, restaurants and institutional kitchens are the places with the most foodbornedisease-associated outbreaks [3]. Since restaurants usually handle a broad range of raw foods, the risk of cross-contamination leading to outbreaks of foodborne disease grows significantly. Cross-contamination in restaurants during food preparation phases can be caused by inadequate hygiene and sanitization methods and contaminated equipment and surfaces. Bacteria can adhere to food contact surfaces, facilitated by the formation of protective film coatings from organic components and nutrients that can impede the sanitization of these surfaces [4; 5]. Contamination detection in restaurants and institutional kitchens is primarily limited to visual examination during the inspection. Another way of testing is swabing food contact surfaces for adenosine triphosphate (ATP) testing or for laboratory culture [6]. In many cases, the visual inspection method is insufficient because the contaminated areas are invisible to the naked eye. When it comes to testing large surfaces, swabs are not the best option. They can be time-consuming and costly since they can only contact a tiny fraction of the surface, even if swabed back and forth. Newer developments in imaging technology and machine learning (ML), especially deep learning algorithms, have been proposed to improve food safety and surface cleanliness. The authors of [7] proposed a system for the early detection of apple bruises using hyperspectral imaging (HSI) with shortwave infrared (SWIR) illumination and a line-scan camera combined with a partial least squares discriminant analysis (PLS-DA) classifier. In [8], real-time detection of parasites in shell-off cooked clams using transillumination imaging and binary decision trees was proposed. A method for detecting the neurotoxin acrylamide in potato chips was suggested by [9], using an SVM classifier on spatial domain statistical features extracted from images. The authors of [10] proposed foreign object detection (FOD) in meat products using a sequential deep learning framework. In [11], a three-level classification of cleanliness in restrooms as dirty, average, and clean using deep convolutional neural networks (CNN) was proposed. In a previous research project, our group combined two deep learning algorithms (EfficientNet-B0 and U-Net) with fluorescence imaging to automatically identify and segment fecal contamination on meat carcasses [12]. In another work, we used Xception and DeepLabV3+ deep learning algorithms with multiwavelength fluorescence imaging to identify and segment contaminated areas in images of equipment and surfaces in the food-service industry [13]. Moreover, our research group has continued to innovate and expand the field with numerous studies that leverage this technology to a variety of cleanliness and sanitization contexts [14; 15; 16]. ML models generally and deep learning algorithms specifically need vast and diversified sets of training data to achieve accurate and reliable performance. These datasets are usually collected from multiple devices, such as cameras, sensors, cell phones, etc., at the interface or edge between the real world and the abstract world of data. The data from these \"edge\" devices are then brought together on a central server or storage system. The ML models use this data to train themselves and eventually predict outcomes with new data. However, these centralized methods can be troublesome if the collected data contain sensitive information or the centralization is too costly.