  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.
