Browsing by Author "Sindhura, S."

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    Federated Split Learning with HyperNetworks for Medical Image Classification
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sindhura, S.; Annappa, B.; Sachin, D.N.
    The integration of Artificial Intelligence (AI) in the medical field has significantly enhanced patient healthcare by enabling the development of sophisticated predictive models. However, the conventional method of building these AI models often requires the aggregation of vast amounts of centralized data, which poses substantial risks to patient data privacy and presents major barriers to scaling these solutions. Federated Learning (FL), a distributed machine learning paradigm, addresses this issue by allowing multiple clients to collaboratively train a model without exchanging their data, thus preserving privacy. Despite its benefits, FL faces significant data and computational heterogeneity challenges. Medical environments frequently feature devices with varied computational capacities, which can severely impede the performance of FL. This paper presents a novel model partitioning approach within the Split Learning (SL) framework by incorporating Dynamic Hypernetworks, which dynamically adjust network parameters in real-time. This method optimizes the’cut layer’—the point at which data is split between client and server—thereby enhancing computational efficiency and significantly reducing the risk of sensitive information leakage. It enables efficient model training on resource-constrained devices and demonstrates superior performance in a medical classification task when compared to traditional centralized, FL, and standard SL methods. The results show improved effectiveness in maintaining data privacy and highlight the potential of this approach in facilitating the broader adoption of AI in healthcare. ©2024 IEEE.