Federated continual learning meets digital twins: A survey on methods, intersections and perspectives

Federated Learning (FL), Continual Learning (CL), and Digital Twins (DTs) have emerged as key paradigms for the development of intelligent, adaptive, and privacy-aware systems in various domains. FL enables collaborative model training across decentralized data sources without sharing raw data, thus ensuring privacy. CL allows models to continuously learn from evolving data streams and adapt to dynamic environments, reducing the need for retraining from scratch. DTs provide accurate virtual representations of physical systems, supporting real-time monitoring, simulation, and predictive maintenance. Combining these paradigms is a recent strategy for building physical systems that are decentralized, adaptive, and continuously improve using real-time data in various contexts. For example, in industries the integration of FCL with DTs can enable factories to learn from new sensor data across distributed sites while preserving sensitive data, adapting to equipment changes, and optimizing maintenance cycles. In mobile edge computing, this combination can enhance service reliability and user experience by updating models based on fresh data and dynamic user behavior. However, their combination also amplifies the inherent challenges, such as model drift, system complexity, and resource constraints, that need to be managed. Despite its promising potential, no existing surveys offer a focused and structured analysis of their intersection. This survey presents the first structured and comprehensive analysis of these three paradigms, highlighting not only existing approaches but also discussing their potential synergies and conflicts, and outlining open research questions that must be addressed to unlock their full potential in real-world applications.