Fenics: A Modular Framework for Security Evaluation in Decentralized Federated Learning

This paper presents Fenics, a modular framework for evaluating the resilience of Decentralized Federated Learning (DFL) networks under adversarial conditions. As a nascent field, DFL raises security challenges in decentralized network settings under adversarial behaviors. To our knowledge, Fenics is the first fully open-source framework of its kind, enabling user-defined topologies, multiple communication protocols, and customizable attack models to study how malicious node placement affects network performance. It integrates core components of DFL, including data distribution, dynamic node participation, and aggregation to establish the DFL architecture. We demonstrate the framework’s capabilities through different use cases under poisoning and delay attacks using the FashionMNIST dataset. The results validate its capability to reveal how node placement affects performance and expose network vulnerabilities. For example, poisoning attacks exhibit topology- dependent impacts, with accuracy dropping by over 55% in certain scenarios, leading to derailed convergence. Additionally, the ex- tensive logging features of the framework enable post-simulation analysis and insightful interpretation. Its modular architecture, simple deployment, and customizable options make it a lightweight yet useful tool for in-depth research on DFL network security.