Zhonghao Chen, Weicong Chen, Duo Zhang, Kibaek Kim, Guanpeng Li, Sheng Di, Xiaoyi Lu
Federated Learning (FL) is a scalable and privacy-preserving paradigm well-suited for edge computing. Real-world FL deployments face substantial systems challenges such as compute variability and communication delays, motivating researchers to leverage simulation before real deployment. Most existing FL simulators, however, struggle to scale efficiently and incur long runtimes even for small workloads. To address this, we present FedDES, a high-fidelity, framework-agnostic discrete-event simulation platform that accurately models the runtime behavior of FL systems, including client training, communication overhead, network dynamics, and aggregation strategies. FedDES supports flexible configurations and diverse aggregation approaches, achieving simulation error within 2% of real deployments and delivering over 1000\texttimes {} speedup compared to prior tools. Large-scale experiments with up to 131,072 clients further show that the aggregation strategy critically affects performance, especially under heterogeneous and variable network conditions typical of edge environments.