Heterogeneous Workload-Based Consumer Resource Recommendation Model for Smart Cities: eHealth Edge-Cloud Connectivity Using Federated Split Learning

Over the past decade, there has been a significant surge in consumer application services and server connectivity, and this trend is expected to double in 2030. The primary contributors to the increased demand for network resources are devices connected through third-party service providers and mobile operators. Many prominent consumer services rely on a client-server architecture, which can introduce latency delays in the communication channel. Additionally, peer-to-peer (P2P) communication places a substantial load on eHealth servers, leading to service delays. In this research paper, we propose a model for scheduling heterogeneous workloads and recommending resources for eHealth edge-cloud connectivity using Federated Split Learning (FSL) model for smart cities. Distributed FSL offers a robust solution for handling both direct and indirect user requests through a distributed mobile core operator stack. This technique empowers eHealth administrators to locally learn optimal policies and make informed decisions by prioritizing resource allocation and scheduling. We demonstrate the effectiveness of this technique through an active simulation server designed for track-driven caching policy and local policy scheduling, ultimately enhancing resource recommendation in eHealth applications. The proposed technique is focused on the development of a heterogeneous workload recommendation system and obtained accuracy of 89.63% over 200 users trails.