Converging Federated Learning, Reinforcement Learning, and Fog Computing for Next-Generation IoT Security
Main article
Abstract
The Internet of Things (IoT) now permeates virtually every layer of modern infrastructure, from connected vehicles and smart factories to wearable medical sensors and household appliances. With this expansion has come a corresponding explosion in the attack surface that adversaries can exploit. Centralised intrusion detection systems, which dominated the previous decade of network security research, are visibly straining under the weight of heterogeneous device populations, latency-sensitive workloads, and the privacy expectations of data owners who are unwilling to surrender raw telemetry to a remote cloud. This paper takes the position that the next generation of IoT security cannot be delivered by any single technique, but must be built on the convergence of three complementary paradigms: federated learning (FL), which keeps training data on the edge while still learning a global model; reinforcement learning (RL), which adapts detection policies to the moving target of evolving attack patterns; and fog computing, which positions computational intelligence within one network hop of the devices it protects. We synthesise the recent literature on each pillar, propose a unifying architecture that integrates the three, and present a representative empirical case study in which the converged framework reaches an average detection accuracy of 97.6 percent across six benchmark datasets, with end-to-end latency below 230 milliseconds and a false-positive rate of 3.1 percent. Beyond the headline numbers, we examine where the convergence approach holds up and where it strains, including non-IID data heterogeneity, asynchronous communication, and adversarial robustness. We conclude with a research agenda that foregrounds energy-aware deployment, zero-day generalisation, and homomorphic-encryption layering.
