FTFormer: Fault-Tolerant Layer Offloading in Edge-Fog-Cloud Federated Split Learning
Jianbo Zhang, Bipul Bikram Thapa, Lena Mashayekhy · 2025
Federated Learning (FL) has emerged as a powerful approach for decentralized model training, yet its deployment in large-scale Internet of Things (IoT) environments faces significant challenges. These challenges include fluctuating bandwidth, frequent node failures, and the resource constraints. Such issues are particularly amplified in multilayer Edge-Fog-Cloud infrastructures. Traditional single-layer FL frameworks often fail to address these issues, leading to disrupted training and poor scalability. To tackle these challenges, we propose FTFormer, a novel fault-tolerant, Transformer-based layer offloading framework designed for multilayer federated split learning. FTFormer leverages a Transformer-based policy network to capture the complex interdependencies among Edge, Fog, and Cloud nodes, including bandwidth variability, compute power, and failure probabilities. Combined with an online Proximal Policy Optimization (PPO) algorithm, the framework dynamically adapts offloading decisions in real time, ensuring efficient task allocation under dynamic conditions. Additionally, FTFormer integrates fault-tolerance mechanisms that enable task re-routing and backup deployment to mitigate the impact of node failures and overloads, maintaining smooth training progress. Using a large-scale event-driven simulator capable of modeling thousands of Edge devices and hundreds of Fog/Cloud nodes, we validate FTFormer's performance. Experimental results show that FTFormer significantly improves training speed, fault resilience, and scalability, outperforming state-of-the-art techniques under high-load and failure-prone scenarios. This work highlights FTFormer as a robust solution for deploying resilient FL in real-world IoT systems.