A Systematic Mapping of federated learning operations and features: Architecture, communication and aggregation models

Abstract

Federated Learning (FL) is a collaborative learning paradigm in which multiple clients train a shared global model without exchanging data. Clients communicate only model updates with a central aggregator. In parallel, Machine Learning Operations (MLOps) streamline the development, deployment, and monitoring of ML systems, while their extension, Federated Learning Operations (FLOps), aims to bring the operational discipline to decentralized and privacy-sensitive settings. This study presents a systematic mapping study (SMS) on FL and FLOps, and clarifies foundational concepts and uncovers new perspectives within this evolving field. We focus on FLOps and FL features: architecture, communication and aggregation models. First research question (RQ) focuses on prevalent FL computing architectures. Second RQ covers data transfer between FL components. Third RQ determines breadth of FLOps application in the scientific literature. Fourth RQ identifies distinct approaches to global model aggregation. Our analysis reveals that Edge-based local training with Cloud-based aggregation is the most adopted architecture, combining Edge privacy and responsiveness with Cloud computational capacity. Communication is enabled through lightweight protocols such as Message Queuing Telemetry Transport (MQTT), but protocol choice depends on constraints. Notably, FLOps remains a rarely addressed topic, indicating a substantial gap in end-to-end support for FL pipelines. Federated Averaging (FedAvg) is the most employed aggregation approach, valued for its simplicity and effectiveness with heterogeneous data. These findings expose critical research gaps in architectural diversity, protocol selection, lifecycle integration and adaptive aggregation, highlighting the need for more cohesive and scalable FL system design in future work.