Enhancing Network Traffic Analysis in O-RAN Enabled Next-Generation Networks Through Federated Multi-Task Learning
Faisal Ahmed, Myungjin Lee, Suresh Subramaniam, Motoharu Matsuura, Hiroshi Hasegawa, Shih‐Chun Lin · 2025
The distributed and disaggregated architecture of next-generation (NextG) networks, including 6G has sparked growing interest in federated learning (FL) as a strategy for enabling privacy-preserving collaborative network traffic analysis at the edge. However, FL encounters significant challenges due to data heterogeneity driven by diverse data distributions across edge nodes, and the scarcity of labeled data further worsened by the time-intensive process of data labeling. Although a few studies have addressed these challenges in network traffic analysis tasks using Multi-Task Learning (MTL), existing approaches pre-dominantly focus on single-task FL, centralized model solutions and overlook the integration of MTL in NextG networks. To bridge this gap, we propose O-FedMTL, a novel framework that combines FL with MTL to enable cooperative traffic analysis within an Open Radio Access Network (O-RAN) environment in NextG networks. MTL enhances FL by mitigating the issues of data heterogeneity and labeled data scarcity through shared knowledge derived from multiple interconnected traffic analysis tasks, i.e., traffic classification, flow duration analysis, and bandwidth estimation. Additionally, MTL offers significant benefits by reducing energy consumption and computation costs at the edge through the simultaneous processing of these tasks within a single model. Extensive experimental results demonstrate that O-FedMTL achieves the target global accuracy for traffic classification, flow duration analysis, and bandwidth estimation with 20, 12, and 23 fewer global communication rounds, respectively, compared to the baseline federated averaging. Additionally, O-FedMTL reduces computation costs by 43% compared to the baseline-combined.