AI 中文总结
针对多智能体系统异质性导致的全局模型联合训练难题，提出测试时协同分类框架，通过分布式协议交换决策统计量，建立误差保证与泛化界并验证其性能。
AI 中文摘要
多智能体系统日益增长的异质性，对跨智能体联合训练全局模型构成重大挑战。与此同时，智能体间的协同推理长期以来被视为网络分布式决策的强大机制。受这些观察的启发，我们提出一种用于多智能体网络分布式二分类的协同框架：一组独立训练的智能体（可能在架构、特征空间或模态上存在差异）在测试时协调行动以形成集体预测，这种协调通过分布式学习协议交换局部决策统计量实现。我们对这种独立训练与协同推理范式开展理论和实验研究，考察其在不同通信预算和分布式学习规则下的性能；在充足、有限轮次及有限精度通信下建立分类误差保证，同时给出PAC风格的泛化界，这些结果刻画了模型异质性、网络拓扑、组合策略和通信约束对预测精度的影响。结合实验结果，它们揭示了所提数据驱动分布式决策框架中独立训练的代价与集体预测的益处。
英文摘要
The increasing heterogeneity of multi-agent systems poses significant challenges for jointly training a global model across agents. At the same time, cooperative inference between agents has long been recognized as a powerful mechanism for distributed decision making over networks. Motivated by these observations, we propose a collaboration framework for distributed binary classification over multi-agent networks, where a set of independently trained agents, potentially differing in architecture, feature space, or modality, coordinate their actions during test time to form collective predictions. This coordination is achieved by exchanging local decision statistics through a distributed learning protocol. We develop a theoretical and experimental study of this independent training and cooperative inference paradigm, and examine its performance under different communication budgets and distributed learning rules. We establish classification error guarantees under sufficient, finite-round, and finite-precision communication, together with PAC-style generalization bounds. These results capture the influence of model heterogeneity, network topology, combination policy, and communication constraints on prediction accuracy. Taken together with the experimental results, they reveal both the price of independent training and the benefit of collective prediction for the proposed distributed decision making framework with models learned from data.
