A Distributed Deep Learning Architecture for Cloud Continuum Deployment in a E-health Scenario

The increasing adoption of deep learning in distributed environments is driving the need for efficient deployment strategies across the cloud–edge continuum. Traditional monolithic models, typically executed in centralized cloud infrastructures, often suffer from high latency, excessive bandwidth consumption, and limited support for privacy-sensitive applications. In this paper, we propose a distributed deep learning framework designed according to the Pipes and Filters architectural pattern, where the inference workflow is decomposed into independent processing stages connected through well-defined intermediate representations. To illustrate the applicability of the proposed approach, we outline a representative scenario: a distributed e-health system, where privacy-aware processing is critical. Finally, we discuss possible work toward the evaluation of the architecture through cloud-edge simulation tools, with the goal of quantitatively assessing performance improvements and guiding future optimization strategies.