Twin2Twin: Digital Twin–Driven Orchestration of Federated Learning in Medical IoT Environments

Abstract

Sixth-generation (6G) networks are expected to integrate intelligence as a native capability, enabling advanced verticals such as digital health (eHealth) supported by large-scale Internet of Medical Things (IoMT) deployments. In this context, Federated Learning (FL) is emerging as a promising paradigm for collaborative model training, allowing distributed medical devices to learn from data while preserving privacy. Nevertheless, conventional approaches such as Federated Averaging (FedAvg) often suffer from unstable convergence and performance degradation in non-independent and non-identically distributed (non-IID) environments, especially under partial and resource-constrained client participation. To address these challenges, we propose a Digital Twin (DT)-enabled FL framework in which the server evolves from a passive aggregator into an active orchestrator of the learning process. The DT consists of a virtual representation of participating devices and their learning dynamics, thereby enabling an informed selection of clients and coordinated updates based on both resource conditions and predictive uncertainty. Building on this orchestration layer, we introduce Twin2Twin (T2T), a hierarchical aggregation strategy that groups clients into similarity-based cohorts and combines their updates in a structured manner, improving training stability in dynamic environments. Specifically, the proposed framework integrates: (i) DT-driven client orchestration that accounts for both device heterogeneity and data informativeness, and (ii) a similarity-aware aggregation mechanism that reduces update variability across training rounds. Experimental results demonstrate that the proposed strategy achieves faster and more stable convergence compared to traditional FL, highlighting the potential of DT-driven orchestration to support reliable and scalable learning in IoMT systems deployed in 6G networks.