Equitable Multi-Task Learning for AI-RANs

TL;DR

This paper proposes an online multi-task learning framework for AI-enabled Radio Access Networks that ensures long-term fairness among heterogeneous users while maintaining efficiency and low computational overhead.

Contribution

It introduces the OWO-FMTL framework combining dual-loop learning and alpha-fairness to achieve equitable performance in dynamic edge scenarios.

Findings

Outperforms existing multi-task learning baselines.

Guarantees diminishing performance disparity over time.

Operates with low computational overhead for edge deployment.

Abstract

AI-enabled Radio Access Networks (AI-RANs) are expected to serve heterogeneous users with time-varying learning tasks over shared edge resources. Ensuring equitable inference performance across these users requires adaptive and fair learning mechanisms. This paper introduces an online-within-online fair multi-task learning (OWO-FMTL) framework that ensures long-term equity across users. The method combines two learning loops: an outer loop updating the shared model across rounds and an inner loop rebalancing user priorities within each round with a lightweight primal-dual update. Equity is quantified via generalized alpha-fairness, allowing a trade-off between efficiency and fairness. The framework guarantees diminishing performance disparity over time and operates with low computational overhead suitable for edge deployment. Experiments on convex and deep learning tasks confirm that OWO-FMTL outperforms existing multi-task learning baselines under dynamic scenarios.