通过物理约束提升联邦学习模型可解释性，保障O-RAN多租户服务风险预测可信
Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction
- 用单调样条约束关键指标，确保模型符合无线物理规律
- 在未见调度策略下仍保持高一致性，误报率降低65%
- 兼顾可解释性与部署效率，适合运营商实时运维场景
O-RAN主动服务保障需提前预测切片级SLA违规。模型须可审计，且跨基站训练时不聚合各切片的KPI数据（因租户敏感）。神经加法模型（NAM）具备可解释性，但原始模型会学习违背无线物理规律的结果（如信道质量提升时风险反而升高），此问题在本地与集中式训练中均存在，非独立同分布的联邦平均进一步加剧。本文提出单调联邦加法模型（Monotone FedNAM），对具有明确物理方向的关键指标使用单调样条表示，其约束在联邦平均中自然保留；其他不确定指标仍保持自由。该模型可作为Non-RT RIC rApp运行，且足够紧凑可部署为Near-RT RIC xApp。实验表明，该模型消除所有单调性违例，受约束形状一致性从0.71提升至1.00，在未见过的调度策略上表现良好，上行流量减少65%，仅损失0.04~0.07 AUC。结果证明，物理约束的联邦加法模型可支撑多租户O-RAN服务保障中的可审计风险推断。
原文摘要 · Abstract (English)
Proactive service assurance in O-RAN requires predicting per-slice SLA violations before they occur. The prediction model must be auditable by operators and must train across base stations without pooling per-slice KPIs, which are commercially sensitive because slices are leased to individual tenants. Neural additive models (NAMs) offer auditability because each KPI contributes through a visible shape function. However, visibility alone does not guarantee physical validity. On the ColO-RAN testbed dataset, unconstrained NAMs learn effects that contradict wireless physics, for example predicting higher risk when channel quality improves. This failure appears under both local and centralized training, and non-IID federated averaging worsens it. We present Monotone FedNAM, a federated additive model in which KPIs with unambiguous physical direction are represented as monotone splines whose constraints survive FedAvg aggregation by construction, while contestable KPIs remain unconstrained. The model trains and operates as a Non-RT RIC rApp and is compact enough for deployment as a Near-RT RIC xApp. Monotone FedNAM eliminates all monotonicity violations, raises constrained shape consistency from 0.71 to 1.00, generalizes to an unseen scheduling policy, and reduces uplink traffic by 65%, at a cost of 0.04 to 0.07 AUC. These results show that physically constrained federated additive models can support auditable SLA risk inference for multi-tenant O-RAN service assurance
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