A Fair Federated Learning Framework for Collaborative Network Traffic Prediction and Resource Allocation

TL;DR

This paper proposes a fair federated learning framework for collaborative network traffic prediction and resource allocation, addressing privacy concerns and fairness issues in heterogeneous network environments.

Contribution

It introduces a novel fair federated learning approach tailored for network traffic prediction and resource management, considering data heterogeneity and bias mitigation.

Findings

Fair federated learning improves prediction accuracy across operators.

The approach results in fairer resource allocation among network connections.

Experimental results on real-world traffic data validate the effectiveness of the framework.

Abstract

In the beyond 5G era, AI/ML empowered realworld digital twins (DTs) will enable diverse network operators to collaboratively optimize their networks, ultimately improving end-user experience. Although centralized AI-based learning techniques have been shown to achieve significant network traffic accuracy, resulting in efficient network operations, they require sharing of sensitive data among operators, leading to privacy and security concerns. Distributed learning, and specifically federated learning (FL), that keeps data isolated at local clients, has emerged as an effective and promising solution for mitigating such concerns. Federated learning poses, however, new challenges in ensuring fairness both in terms of collaborative training contributions from heterogeneous data and in mitigating bias in model predictions with respect to sensitive attributes. To address these challenges, a fair FL framework is proposed for collaborative network traffic prediction and resource allocation. To demonstrate the effectiveness of the proposed approach, noniid and imbalanced federated datasets based on real-word traffic traces are utilized for an elastic optical network. The assumption is that different optical nodes may be managed by different operators. Fairness is evaluated according to the coefficient of variations measure in terms of accuracy across the operators and in terms of quality-of-service across the connections (i.e., reflecting end-user experience). It is shown that fair traffic prediction across the operators result in fairer resource allocations across the connections.