让边缘AI网络中不同用户公平高效地共享学习资源
Equitable Multi-Task Learning for AI-RANs
- 内外双层学习机制，动态调整用户优先级
- 长期性能差异持续减小，保障多用户公平性
- 轻量级设计适合边缘设备部署，实测效果更优
人工智能赋能的无线接入网（AI-RANs）需在共享边缘资源上服务多样化、随时间变化的学习任务。确保各用户间推理性能的公平性，依赖于自适应且公平的学习机制。本文提出一种在线之内的公平多任务学习（OWO-FMTL）框架，实现用户间的长期公平性。该方法结合外层循环（跨轮次更新共享模型）与内层循环（每轮内通过轻量级原对偶更新重平衡用户优先级）。公平性通过广义alpha-公平性度量，可在效率与公平间权衡。框架保证性能差距随时间递减，且计算开销低，适合边缘部署。在凸学习与深度学习任务上的实验表明，该方法在动态场景下优于现有多任务学习基线。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。