ModularFed: Leveraging Modularity in Federated Learning Frameworks

TL;DR

ModularFed is a flexible, modular framework designed to improve the development, testing, and comparison of federated learning approaches by providing standardized protocols and supporting third-party extensions.

Contribution

It introduces a comprehensive, adaptable architecture with protocols that enable modularity, extendability, and fair comparison of FL techniques, addressing limitations of existing frameworks.

Findings

Supports multiple FL paradigms and third-party integrations

Enables consistent replication of FL issues for fair comparison

Addresses FL domains like resource monitoring and client selection

Abstract

Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms. However, the standards of the available frameworks can no longer sustain the rapid advancement and hinder the integration of FL solutions, which can be prominent in advancing the field. In this paper, we propose ModularFed, a research-focused framework that addresses the complexity of FL implementations and the lack of adaptability and extendability in the available frameworks. We provide a comprehensive architecture that assists FL approaches through well-defined protocols to cover three dominant FL paradigms: adaptable workflow, datasets distribution, and third-party application support. Within this architecture, protocols are blueprints that strictly define the framework's components' design, contribute to its flexibility, and strengthen its infrastructure. Further, our protocols aim to enable modularity in FL, supporting third-party plug-and-play architecture and dynamic simulators coupled with major built-in data distributors in the field. Additionally, the framework support wrapping multiple approaches in a single environment to enable consistent replication of FL issues such as clients' deficiency, data distribution, and network latency, which entails a fair comparison of techniques outlying FL technologies. In our evaluation, we examine the applicability of our framework addressing three major FL domains, including statistical distribution and modular-based approaches for resource monitoring and client selection.