Open Access

REVIEW

From Trust to Efficiency: Challenges, Optimizations, and the Hyper-Learning Framework for IoT Ecosystems

Priyanka Halder, Gopikrishnan Sundaram^*

School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

* Corresponding Author: Gopikrishnan Sundaram. Email:

Journal on Internet of Things 2026, 8, 127-153. https://doi.org/10.32604/jiot.2026.073962

Received 29 September 2025; Accepted 30 January 2026; Issue published 29 May 2026

Abstract

The need for intelligent learning frameworks that can function under stringent limitations relating to privacy, energy, scalability, and trust has increased due to the Internet of Things’ (IoT) and the Internet of Artificial Things’ (IoAT) explosive expansion. Federated Learning (FL), which allows collaborative model training without sharing raw data, has become a potential approach. Non-IID data delivery, inconsistent client engagement, vulnerability to poisoning assaults, and low resource knowledge are among of the significant obstacles that FL alone must overcome. Blockchain integration adds extra overhead in terms of latency, energy consumption, and scalability, but it has been suggested to address trust, auditability, and integrity through decentralised validation and immutable logging. DRL, on the other hand, has demonstrated a great deal of promise for adaptive decision-making, but it is frequently researched separately from blockchain and FL systems. With an emphasis on the function of DRL in enhancing system performance, this survey offers a thorough and organised analysis of blockchain-enabled federated learning for IoT and IoAT ecosystems. We methodically examine current consensus protocols, DRL-driven optimisation techniques, blockchain-based security measures, and distributed learning architectures. In a variety of IoT situations, such as healthcare, industrial IoT, UAV networks, and smart cities, important issues pertaining to energy efficiency, communication overhead, incentive mechanisms, block size management, and participant trust are critically explored. Additionally, a comparison of privacy-preserving methods like Homomorphic Encryption and Differential Privacy is given, emphasising their trade-offs and appropriateness for contexts with limited resources. This survey presents a Hyper-Learning Framework that tightly integrates FL, blockchain, and DRL inside a single control loop based on the gaps found. The system seeks to maintain efficiency and privacy while facilitating scaled learning, secure collaboration, and adaptive resource management. In order to steer the creation of reliable, sustainable, and intelligent IoT learning systems, open research directions and upcoming difficulties are finally discussed.

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Keywords

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