Edge computing has emerged as a key technology for enabling real-time data processing in various applications such as Industrial Internet of Things (IIoT), smart cities, and autonomous systems. However, the distributed nature of edge computing makes it particularly vulnerable to system faults, such as hardware failures, network outages, and data corruption. To address these challenges, fault-tolerant strategies are essential for ensuring the reliability and resilience of edge systems. Furthermore, federated learning (FL) offers a decentralized approach to machine learning, which can enhance the resilience of edge computing environments by allowing edge devices to collaborate on training models without relying on a central server. This paper explores the integration of fault-tolerant strategies with federated learning to provide a comprehensive, resilient framework for edge computing. Various aspects are compared for fault-tolerant mechanisms with federated learning frameworks to analyze their effectiveness in enhancing system reliability and ensuring real-time performance in the face of failures.