Fair Resource Allocation for Fleet Intelligence

2025-09-01

AI Summary

To address inefficient and unfair resource allocation in cloud-assisted multi-agent systems—caused by neglecting agent heterogeneity and environmental complexity—this paper proposes Fair-Synergy, an open-source framework. Methodologically, it introduces the first concave modeling of the accuracy–resource relationship and constructs a multidimensional machine learning utility space incorporating model parameters, data volume, and task complexity to quantify fairness impacts across heterogeneous agents. Extensive experiments are conducted on benchmark datasets (MNIST, CIFAR, BDD, GLUE) using diverse models (BERT, VGG16, MobileNet, ResNets) for multi-agent inference and learning. Results demonstrate that Fair-Synergy improves system-wide performance and fairness by 25% in inference and 11% in learning over state-of-the-art baselines, significantly advancing resource-aware fairness in distributed intelligent systems.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Ensuring fair resource allocation in cloud-assisted multi-agent intelligence systems
• Addressing inefficient distribution due to diverse computational capabilities and environments
• Extending traditional approaches to multidimensional machine learning utility landscape

Innovation

• Fair-Synergy framework ensures fair resource allocation
• Extends allocation to multidimensional ML utility landscape
• Outperforms benchmarks by 25% in inference scenarios