GLow - A Novel, Flower-Based Simulated Gossip Learning Strategy
Aitor Belenguer, Jose Antonio Pascual, Javier Navaridas · Journal of Parallel and Distributed Computing · 2026
• Creation of a Decentralized Federated Learning (Gossip Learning) strategy to simulate fully distributed agent configurations. • Deploy and evaluate custom network scenarios and assess how interconnection among agents affect distributed systems convergence. • Experimentation with MNIST and CIFAR10 datasets and 8, 16 network agents to second the viability of the designed Gossip Learning system. • Real-world application in the cybersecurity domain - Network Intrusion Detection Systems. Fully decentralized learning algorithms are still in an early stage of development. Creating modular Decentralized Federated Learning strategies, as Gossip Learning, is not trivial due to convergence challenges and Byzantine faults intrinsic in systems of decentralized nature. Our contribution provides a novel means to simulate custom Gossip Learning systems by leveraging the state-of-the-art Flower Framework. Specifically, we introduce GLow, allows researchers to train and assess scalability and convergence of devices, across custom network topologies, before making a physical deployment. The Flower Framework is selected for being a simulation featured library with a very active community on Federated Learning research. However, Flower exclusively includes vanilla Federated Learning strategies and, thus, is not originally designed to perform simulations without a centralized authority. GLow is presented to fill this gap and make simulation of Gossip Learning systems possible. The results achieved by GLow on the MNIST and CIFAR10 datasets show accuracies above 0.98 and 0.75, respectively, using double ring or denser topologies. More importantly, GLow performs similarly in terms of accuracy and convergence to its analogous Centralized and Federated approaches. Additional evidence is provided including irregular topologies as well as a cybersecurity use case, where a potential application of Decentralized Federated Learning is deployed using the TON_IOT dataset.