Paradigm Shift Toward Distributed Learning in IoT Intelligence: A Comprehensive Survey of Opportunities and Challenges

The rapid evolution of beyond fifth-generation (B5G) and sixth-generation (6G) networks is reshaping mobile edge computing (MEC) to support large-scale, heterogeneous Internet of Things (IoT) deployments and complex cyber-physical systems (CPS). Conventional data-driven intelligence in MEC traditionally relies on centralized learning paradigms that often fail to meet the privacy, latency, scalability, and adaptability requirements in distributed and resource-constrained environments. To address these shortcomings, the objective of our work in this paper is to investigate the paradigm shift toward distributed learning and demonstrate how its co-design with emerging communication and system-level technologies can enable scalable and trustworthy intelligence for next-generation IoT and CPS. Building on this objective, we conduct a systematic survey of recent studies and analyze twelve key enabling technologies, including concept drift adaptation, transformers, TinyML, blockchain, integrated sensing and communication (ISAC), digital twins, explainable AI, federated learning and unlearning, adversarial ML, meta-learning, and multi-armed bandits. The surveyed literature is organized using a unified taxonomy and an integrated conceptual pipeline, which clarifies how these enablers interact across sensing, communication, computation, trust, and adaptation layers of IoT and CPSs. The main outcomes of this study include: (i) a comprehensive taxonomy characterizing enabling technologies for distributed edge intelligence, (ii) a comparative synthesis of representative works highlighting common architectural patterns and evaluation practices, and (iii) the identification of research gaps, critical trade-offs, and open challenges, particularly related to model robustness, energy efficiency, data heterogeneity, and secure real-time inference. Overall, this survey establishes a structured foundation and forward-looking roadmap for designing scalable, privacy-preserving, and intelligent distributed learning systems in future B5G- and 6G-enabled IoT and CPS environments.