Editing Federated Learning (section)

=== 5.2.3 Hierarchical Architecture ===

Hierarchical FL is a hybrid architecture that introduces one or more intermediary layers—often called edge servers or aggregators—between clients and the global coordinator. In this model, clients are organized into logical or geographical groups, with each group connected to an edge server. Clients send their local model updates to their respective edge aggregator, which performs preliminary aggregation. The edge servers then send their aggregated results to the cloud server, where final aggregation occurs to produce the updated global model.

This multi-tiered architecture is designed to address the scalability and efficiency challenges inherent in centralized systems while avoiding the coordination overhead of full decentralization. Hierarchical FL is especially well-suited for edge computing environments where data, clients, and compute resources are distributed across structured clusters, such as hospitals within a healthcare network or base stations in a telecommunications infrastructure.

One of the key advantages of hierarchical FL is communication optimization. By aggregating locally at edge nodes, the amount of data transmitted over wide-area networks is significantly reduced. Additionally, this model supports region-specific model personalization by allowing edge servers to maintain specialized sub-models adapted to local client behavior. Hierarchical FL also enables asynchronous and fault-tolerant training by isolating disruptions within specific clusters. However, this architecture still depends on reliable edge aggregators and introduces new challenges in cross-layer consistency, scheduling, and privacy preservation across multiple tiers.<sup>[1]</sup><sup>[3]</sup>