Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique’s inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, we provide a detailed description of FL architecture, models, descriptions of the performance achieved by FL models, and how results compare with traditional Machine Learning (ML) models. Additionally, we discuss the security benefits, highlighting two primary forms of privacy-preserving techniques, including homomorphic encryption and differential privacy. Finally, we provide some background information and context regarding where the contributions lie. The background information is organized into the following categories: architecture/setup type, data-related topics, security, and learning types. While progress has been made within the field of FL and medical imaging, much room for improvement and understanding remains, with an emphasis on security and data issues remaining the primary concerns for researchers. Therefore, improvements are constantly pushing the field forward. Finally, we highlighted the challenges in deploying FL in medical imaging applications and provided recommendations for future directions.

Medical Imaging Applications of Federated Learning

Role of Artificial Intelligence Techniques and Neuroimaging Modalities in Detection of Parkinson’s Disease: A Systematic Review

Federated learning, a distributed machine learning method, trains statistical models over remote devices or servers with local data, without exchanging data samples. It preserves patient data securely behind hospital firewalls, sharing only anonymous model parameters, enhancing privacy and security. This innovative approach exchanges only untraceable learned feature representations. This study introduces a new federated learning framework an innovative aggregation methods based on Particle Swarm Optimization (PSO) algorithm for breast cancer prognosis. To safeguard patient data privacy, a privacy mechanism is implemented at the user's end. By utilizing the federated learning model, they've addressed the data scarcity problem, leading to improved accuracy in breast cancer progno-sis. Federated learning, as mentioned earlier, protects personal data by sending model parameters to agents instead of raw data to a central node, keeping data localized. However, the risk of malicious nodes injecting fake data into the global model is a concern. To address this, we need a verification mechanism to authenticate senders and their training data while preserving data privacy. We use zero-knowledge proof (ZKP) for verification without exposing raw data for this part. At the outset of the federated learning process, each client computes the Merkle tree root hash of their local training data and submits it to the federated learning contract for participation. In subsequent training rounds, the client's compiler checks the integrity of their data by comparing the Merkle tree root hash to the initial one. If they match, a trace file is generated and sent for proof generation, verification, fact creation, and registration. Leveraging the TCGAbiolinks package, we conducted experiments demonstrating the effectiveness of our approach with real data. Learner clients enhance their local models using a PSO algorithm, achieving remarkable results in binary classification tasks with 400 features while preserving patient data privacy via ZKP integration. Our framework reaches 97% accuracy with eight clients, compared to a comparable system's 82%, highlighting its practical applicability and efficiency in real-world scenarios, particularly when dealing with larger numbers of participants.

Privacy-Preserving Genomic Analysis via PSO-Driven Federated Learning on Blockchain

Parkinson’s Disease is the second most prevalent neurodegenerative disorder, currently affecting as high as 3% of the global population. Research suggests that up to 80% of patients manifest phonatory symptoms as early signs of the disease. In this respect, various systems have been developed that identify high risk patients by analyzing their speech using recordings obtained from natural dialogues and reading tasks conducted in clinical settings. However, most of them are centralized models, where training and inference take place on a single machine, raising concerns about data privacy and scalability. To address these issues, the current study migrates an existing, state-of-the-art centralized approach to the concept of federated learning, where the model is trained in multiple independent sessions on different machines, each with its own dataset. Therefore, the main objective is to establish a proof of concept for federated learning in this domain, demonstrating its effectiveness and viability. Moreover, the study aims to overcome challenges associated with centralized machine learning models while promoting collaborative and privacy-preserving model training. 

Exploring Federated Learning for Speech-based Parkinson’s Disease Detection

Detection of Parkinson’s Disease from T2-Weighted Magnetic Resonance Imaging Scans Using EfficientNet-V2

Parkinson’s disease (PD) is a degenerative ailment caused by the loss of nerve cells in the brain region known as the Substantia Nigra, which governs movement. In numerous research papers, traditional machine learning techniques have been utilized for the purpose of PD detection. However, traditional ML algorithms always put a risk on the sensitivity of patients’ data and privacy. This research proposes a novel approach to detecting PD by preserving privacy and security through Federated Learning (FL). FL may train a single algorithm across numerous decentralized local servers as an improved version of the ML approach instead of trading gradient information. The proposed model has been tested and evaluated by using three CNN models (VGG19, VGG16 & InceptionV3) in this research, and within these models, VGG19 has the best accuracy of 97%. The result demonstrates that this model is very accurate for detecting PD by preserving one’s privacy and security using Federated Learning.

A Federated Learning Based Privacy Preserving Approach for Detecting Parkinson’s Disease Using Deep Learning

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A Federated Learning Based Privacy Preserving Approach for Detecting Parkinson’s Disease Using Deep Learning