Modular Federated Learning: A Meta-Framework Perspective

TL;DR

This paper introduces a meta-framework perspective for federated learning, conceptualizing it as a modular system with core components like communication, security, and optimization, to better understand and advance the field.

Contribution

It presents a novel taxonomy distinguishing Aggregation from Alignment and offers a structured, modular approach to analyze and develop federated learning systems.

Findings

Proposes a meta-framework for federated learning with modular components.

Introduces the concept of Alignment as a fundamental operator alongside Aggregation.

Provides insights into open challenges and future research directions.

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.