Scheduling In-Band Network Telemetry With Convergence-Preserving Federated Learning
Scheduling In-Band Network Telemetry With Convergence-Preserving Federated Learning
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DOI：
10.1109/tnet.2023.3253302
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Yibo Jin;Lei Jiao;Mingtao Ji;Zhuzhong Qian;Sheng Z. Zhang;Ning Chen;Sanglu Lu
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文献类型：
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作者：
Yibo Jin;Lei Jiao;Mingtao Ji;Zhuzhong Qian;Sheng Z. Zhang;Ning Chen;Sanglu Lu
通过基于带内网络遥测（INT）的数据收集在分布式站点上进行联合学习面临着关键挑战，包括不同频率的控制决策，正在训练的模型的收敛以及随着时间的推移耦合的资源配置。为了研究这个问题，我们制定了一个非线性混合整数规划，以优化长期INT开销，资源成本和联邦学习成本。然后，我们设计了多项式时间在线算法来解决这个问题，只有可观察的输入在飞行中，具有懒惰意识的资源自适应，基于在线学习的INT流选择和模型聚合控制，以及期望保持随机相关舍入。我们严格证明了我们的方法对离线最优的参数化常数竞争比，以及从长远来看消失的时间平均约束违反。通过广泛的跟踪驱动评估，我们证实了我们的方法在降低总成本方面优于其他替代方法，以及我们训练的模型在解决真实的机器学习问题方面的有效性，平均将实时成本降低了34%。
Conducting federated learning across distributed sites with In-Band Network Telemetry (INT) based data collection faces critical challenges, including control decisions of different frequencies, convergence of the models being trained, and resource provisioning coupled over time. To study this problem, we formulate a non-linear mixed-integer program to optimize the long-term INT overhead, resource cost, and federated learning cost. We then design polynomial-time online algorithms to solve this problem with only observable inputs on the fly, featuring laziness-aware resource adaption, online-learning-based INT flow selection and model aggregation control, as well as expectation-preserving randomized dependent rounding. We rigorously prove the parameterized-constant competitive ratio of our approach against the offline optimum, and the time-averaged constraint violation that vanishes in the long run. With extensive trace-driven evaluations, we confirm the superiority of our approach over other alternative approaches for reducing total cost and the efficacy of our trained models for solving real machine learning problems, reducing the real-time cost by 34% on average.