# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 1 Introduction  
Machine learning (ML) has revolutionized various fields, particularly healthcare, where accurate disease diagnosis is critical. In recent years, ML models have become capable of diagnosing and classifying diseases as well as or better than healthcare experts [1]. For example, the use of VGG-Net models like VGG-16 and VGG-19 has become increasingly common, especially within the ocular disease/healthcare space due to the cost of devices required to classify these diseases [2]. However, many large models require large amounts of data to be effective. This makes training these large models exceptionally difficult, leading to state-of-the-art models training collaboratively (due to scarcity of data) [3]. This practice of training in collaboration often relies on training large volumes of data in a single (centralized) location--this practice is known as centralized learning (CL) [4]. However, in almost all commercial fields/industries, using CL is often impossible because of the many privacy regulations regarding personal data [5]. The Health InsurancePortability and Accountability Act (HIPAA) in the US limits data sharing in FL by enforcing strict privacy and security rules [6]. The General Data Protection Regulation (GDPR) in the EU restricts cross-border data use and requires user consent, complicating FL [7]. Canada's PIPEDA mandates transparency and consent, adding compliance challenges for FL [8]. These regulations restrict personal health data's free flow, making CL often unfeasible. Additionally, the computational overhead when using CL can be prohibitively expensive, as all training would typically be performed on a singular server. This centralized approach strains resources and is often more computationally inefficient, making it less practical in healthcare scenarios where large-scale data need to be processed efficiently and securely to develop effective, high-performing ML models [9; 10]. Figure 1 demonstrates the differences between CL and federated learning (FL). As can be seen from Figure 1, CL gathers all training data in one place, a central server, where a single model is trained and then distributed. In contrast, FL distributes the training process by allowing users to train models locally on their own devices without sharing their raw data. Only model updates are sent to a central server, aggregating them to improve a global model that is then shared with the users. This approach enhances privacy, reduces communication overhead, and allows for more personalized models compared to the centralized approach [12; 13]. FL is a relatively new collaborative ML technique that can sidestep almost all the mentioned issues with ease [14; 15]:  
* FL trains local models on each user's data and aggregates models together to create a global model (the global model is created based on combining model weights, instead of combining datasets, which violates data privacy regulations). * This ensures privacy when training models through techniques such as differential privacy. Figure 1: Architecture Comparison: (**a**) CL; (**b**) FL [11]. * FL often adds random noise (varying fluctuations to model weights during training) to datasets to prevent backtracking and reverse engineering of the models to reveal sensitive information about any individual patient used in the data (often using the differential privacy method) [16]. * Computing power can become distributed at scale and reduce bandwidth requirements (computations for training are split across the different clients participating in FL instead of just a singular centralized server). To address the healthcare dataset sharing and collaborative model training challenges posed by privacy laws and regulations, this study developed a novel FL architecture. Unlike traditional FL approaches, which often suffer from up to a 55% loss in model accuracy, the methods utilized in this architecture achieve only an average of 1.85% loss in accuracy (nearly 92% accuracy improvement) when applied to ophthalmology [17].