用联邦学习解决大规模异构群体的资源分配难题。
Federated Learning for Discrete Optimal Transport with Large Population under Incomplete Information
- 分两阶段设计：已知分布时用分布式算法，未知时用联邦学习。
- 在隐私保护下实现大规模异构群体的最优运输方案。
- 适合需隐私保护的大规模资源调度场景，如跨机构协同。
最优传输是高效分配资源的强大框架，但传统模型在大规模异构群体中难以有效扩展。本文提出一种离散最优传输框架，用于处理具有类型分布的大规模异构目标群体。针对两种情形：一是目标类型分布已知，提出完全分布式算法实现最优资源配置；二是类型分布未知，设计基于联邦学习的方法，在保障隐私的前提下高效计算最优传输方案。通过案例研究评估了所提学习算法的性能。
原文摘要 · Abstract (English)
Optimal transport is a powerful framework for the efficient allocation of resources between sources and targets. However, traditional models often struggle to scale effectively in the presence of large and heterogeneous populations. In this work, we introduce a discrete optimal transport framework designed to handle large-scale, heterogeneous target populations, characterized by type distributions. We address two scenarios: one where the type distribution of targets is known, and one where it is unknown. For the known distribution, we propose a fully distributed algorithm to achieve optimal resource allocation. In the case of unknown distribution, we develop a federated learning-based approach that enables efficient computation of the optimal transport scheme while preserving privacy. Case studies are provided to evaluate the performance of our learning algorithm.
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