Simplifying Federated Learning Deployment: Design and Implementation of the FLeeT Framework

Over the past few years, Federated Learning (FL) has emerged as a prominent paradigm for distributed model training, enabling collaborative learning across heterogeneous edge environments while inherently supporting privacy preservation. Although several frameworks have emerged to support FL workflows, deployment remains a complex and technically demanding phase, often requiring advanced expertise in system administration, virtualization, and networking. This complexity limits accessibility and slows down experimentation and adoption in broader research and industry contexts. To address these limitations, this work introduces a novel deployment toolkit designed to streamline the setup and execution of FL scenarios. Unlike existing solutions, our tool prioritizes ease of use, intuitive configuration, and a gentle learning curve, enabling users to design and launch sophisticated simulations and network topologies without requiring deep system-level knowledge. Through its modular architecture and lightweight orchestration model, the toolkit supports scalable deployments, complex federation strategies, and hybrid edge-cloud environments with minimal overhead.