Decentralized Federated Deep Learning for Early Detection of Breast Cancer in Heterogeneous Health Records

One of the main causes of death for women worldwide is still breast cancer, which emphasises the need for early and precise diagnosis. Because of institutional silos, traditional centralised machine learning algorithms for cancer diagnosis frequently confront obstacles with data privacy, complying with regulations, and low data variety. This study suggests a decentralised federated learning (FL)-based, productive, secure privacy-based breast cancer detection framework that can address these shortcomings. While maintaining data locality, the recommended method makes use of an assortment of datasets from open-access medical resources such as Kaggle and the UCI Repository. To guarantee compatibility and privacy, preprocessing involves standardising formats, suppressing noise, and anonymising medical information. Local model training is performed amongst several distributed clients employing the Federated Averaging (FedAvg) and Federated Optimisation (FedOpt) algorithms. These locally trained models are securely transferred and aggregated using privacy-preserving approaches like secure aggregation and Byzantine tolerance for faults to create a global model that does not share raw patient data. The system architecture consists of model initialisation, decentralised local training, secure update exchange, and aggregation. Validation datasets are evaluated in order to determine their performance and generalisation. Furthermore, differential privacy and secure multiparty computation are combined to improve data confidentiality and conformance to legal as well as ethical standards. This federated tactics improves model robustness by using heterogeneous data while nonetheless guaranteeing institutional autonomy and regulatory compliance. The experimental results demonstrate the framework's adaptability for real-world applications in medicine. The subsequent phases of this study will look into integrating with multi-modal data sources and edge computing, with the goal of creating a scalable and explainable AI-based diagnosing infrastructure for healthcare.