The emerging availability of trained machine learning models has put forward the novel concept of Machine Learning Model Market in which one can harness the collective intelligence of multiple well-trained models to improve the performance of the resultant model through one-shot federated learning and ensemble learning in a data-free manner. However, picking the models available in the market for ensemble learning is time-consuming, as using all the models is not always the best approach. It is thus crucial to have an effective ensemble selection strategy that can find a good subset of the base models for the ensemble. Conventional ensemble selection techniques are not applicable, as we do not have access to the local datasets of the parties in the federated learning setting. In this paper, we present a novel Data-Free Diversity-Based method called DeDES to address the ensemble selection problem for models generated by one-shot federated learning in practical applications such as model markets. Experiments showed that our method can achieve both better performance and higher efficiency over 5 datasets and 4 different model structures under the different data-partition strategies.

翻译：随着预训练机器学习模型的日益普及，机器学习模型市场这一新概念应运而生。在该市场中，用户可通过一次性联邦学习和集成学习，以无数据方式利用多个优质模型的集体智慧提升最终模型的性能。然而，从市场中选择用于集成学习的模型会耗费大量时间，因为并非所有模型都是最佳选择。因此，制定一种高效的集成选择策略至关重要——该策略需能筛选出适合集成的基础模型子集。由于联邦学习场景下无法访问各参与方的本地数据集，传统集成选择技术在此不再适用。本文提出了一种名为DeDES的新型无数据多样性方法，用于解决模型市场等实际应用中由一次性联邦学习生成的模型的集成选择问题。实验表明，在5个数据集、4种不同模型结构及多种数据划分策略下，本方法均能同时实现更优的性能与更高的效率。