FL-as-API: A Service-Oriented Architecture for Heterogeneous Federated Learning

Bustincio R.; Marquez Solis P.Y.; De Souza A.M.; Bittencourt L.F. · Proceedings of the 18th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2025 (2025) · DOI: 10.1145/3773274.3774679

Resumen

Federated Learning (FL) enables model training across decentralized datasets, preventing the centralization of sensitive raw data. Despite robust algorithmic progress, operational adoption is often hindered by technological heterogeneity in collaborative environments, where different teams employ diverse Machine Learning (ML) frameworks (e.g., PyTorch, TensorFlow, JAX), whose differences in internal model representation hinder direct collaboration. This paper presents an empirical analysis of FL-as-API, a Service-Oriented Architecture (SOA) for FL designed to operate in these heterogeneous environments. The system enables inter-framework operability via an orchestrator that performs real-time weight normalization, assuming the responsibility for "translating"models between different formats. The architecture's performance is empirically evaluated on a purpose-built FL environment featuring clients implemented in both PyTorch and TensorFlow, demonstrating that: 1) overall system performance (measured in rounds per minute) is primarily constrained by the computational time of the slowest client, rather than by communication or aggregation; 2) the cost of interoperability (server-side weight normalization) is quantitatively low compared to the total round duration; and 3) the service-level architecture enables the effective identification of client stragglers, isolating performance bottlenecks at the individual machine level. This work provides a quantitative evaluation of the costs and benefits of architectural flexibility in real-world FL systems.

Instituciones: Universidad Continental