Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions

Abstract

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.