Fair Resource Allocation for Fleet Intelligence

• URL: http://arxiv.org/abs/2509.03353v1
• Date: Tue, 02 Sep 2025 03:20:41 GMT
• Title: Fair Resource Allocation for Fleet Intelligence
• Authors: Oguzhan Baser, Kaan Kale, Po-han Li, Sandeep Chinchali,
• Abstract summary: We open-sourced Fair-Synergy, an algorithmic framework to ensure fair resource allocation across fleet intelligence.<n>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.<n>We demonstrate that Fair-Synergy outperforms standard benchmarks by up to 25% in multi-agent inference and 11% in multi-agent learning settings.
• Score: 6.70517744733229
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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