Fair Resource Allocation for Fleet Intelligence
This addresses resource allocation inefficiencies for fleet intelligence systems, offering a domain-specific solution that is incremental by extending traditional approaches.
The paper tackles the problem of inefficient and unfair resource allocation in cloud-assisted multi-agent intelligence by proposing Fair-Synergy, an algorithmic framework that ensures fair distribution based on agents' computational capabilities and environments, demonstrating performance improvements of up to 25% in inference and 11% in learning settings.
Resource allocation is crucial for the performance optimization of cloud-assisted multi-agent intelligence. Traditional methods often overlook agents' diverse computational capabilities and complex operating environments, leading to inefficient and unfair resource distribution. To address this, we open-sourced Fair-Synergy, an algorithmic framework that utilizes the concave relationship between the agents' accuracy and the system resources to ensure fair resource allocation across fleet intelligence. We extend traditional allocation approaches to encompass a multidimensional machine learning utility landscape defined by model parameters, training data volume, and task complexity. We evaluate Fair-Synergy with advanced vision and language models such as BERT, VGG16, MobileNet, and ResNets on datasets including MNIST, CIFAR-10, CIFAR-100, BDD, and GLUE. We demonstrate that Fair-Synergy outperforms standard benchmarks by up to 25% in multi-agent inference and 11% in multi-agent learning settings. Also, we explore how the level of fairness affects the least advantaged, most advantaged, and average agents, providing insights for equitable fleet intelligence.