Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions
Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions
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DOI：
10.1109/infocom53939.2023.10229014
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Fei Wang;Lei Jiao;Konglin Zhu;Xiaojun Lin;Lei Li
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文献类型：
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作者：
Fei Wang;Lei Jiao;Konglin Zhu;Xiaojun Lin;Lei Li
云边缘系统是紧急需求响应（EDR）的重要参与者，有助于维持电网稳定性和供需平衡。然而，随着用户越来越多地在云边缘系统中执行人工智能（AI）工作负载，现有的EDR管理并不是针对AI工作负载而设计的，因此面临着能源消耗和AI模型准确性之间的复杂权衡，AI模型量化导致的模型准确性下降，AI训练期限的限制以及AI任务到达的不确定性等关键挑战。在本文中，针对联邦学习（FL），我们设计了一个基于拍卖的方法来克服所有这些挑战。首先，我们建立了一个非线性混合整数规划的长期社会福利优化。然后，我们提出了一种新的算法方法，生成候选人的训练计划，重新制定了原来的问题到一个新的时间表选择问题，并解决了这个新的问题，使用在线原始对偶为基础的算法，精心嵌入的支付设计。我们进一步严格证明，我们的方法实现了真实性和个人理性，并导致一个恒定的竞争比的长期社会福利。通过对真实数据和设置的广泛评估，我们已经验证了我们的方法比多种替代方法具有上级实际性能。
Cloud-edge systems are important Emergency Demand Response (EDR) participants that help maintain power grid stability and demand-supply balance. However, as users are increasingly executing artificial intelligence (AI) workloads in cloud-edge systems, existing EDR management has not been designed for AI workloads and thus faces the critical challenges of the complex trade-offs between energy consumption and AI model accuracy, the degradation of model accuracy due to AI model quantization, the restriction of AI training deadlines, and the uncertainty of AI task arrivals. In this paper, targeting Federated Learning (FL), we design an auction-based approach to overcome all these challenges. We firstly formulate a nonlinear mixed-integer program for the long-term social welfare optimization. We then propose a novel algorithmic approach that generates candidate training schedules, reformulates the original problem into a new schedule selection problem, and solves this new problem using an online primal-dual-based algorithm, with a carefully embedded payment design. We further rigorously prove that our approach achieves truthfulness and individual rationality, and leads to a constant competitive ratio for the long-term social welfare. Via extensive evaluations with real-world data and settings, we have validated the superior practical performance of our approach over multiple alternative methods.