Modular Federated Learning: A Meta-Framework Perspective

• URL: http://arxiv.org/abs/2505.08646v1
• Date: Tue, 13 May 2025 15:04:55 GMT
• Title: Modular Federated Learning: A Meta-Framework Perspective
• Authors: Frederico Vicente, Cláudia Soares, Dušan Jakovetić,
• Abstract summary: Federated Learning (FL) enables distributed machine learning training while preserving privacy.<n>Despite its rapid advancements, FL remains a complex and multifaceted field.<n>We introduce a meta-framework perspective, conceptualising FL as a composition of modular components.
• Score: 1.9067130892610522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) enables distributed machine learning training while preserving privacy, representing a paradigm shift for data-sensitive and decentralized environments. Despite its rapid advancements, FL remains a complex and multifaceted field, requiring a structured understanding of its methodologies, challenges, and applications. In this survey, we introduce a meta-framework perspective, conceptualising FL as a composition of modular components that systematically address core aspects such as communication, optimisation, security, and privacy. We provide a historical contextualisation of FL, tracing its evolution from distributed optimisation to modern distributed learning paradigms. Additionally, we propose a novel taxonomy distinguishing Aggregation from Alignment, introducing the concept of alignment as a fundamental operator alongside aggregation. To bridge theory with practice, we explore available FL frameworks in Python, facilitating real-world implementation. Finally, we systematise key challenges across FL sub-fields, providing insights into open research questions throughout the meta-framework modules. By structuring FL within a meta-framework of modular components and emphasising the dual role of Aggregation and Alignment, this survey provides a holistic and adaptable foundation for understanding and advancing FL research and deployment.

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