论文标题
对生成模型的松散耦合联合学习
Loosely Coupled Federated Learning Over Generative Models
论文作者
论文摘要
提出了联合学习（FL），以在不上传私人数据的情况下实现各种客户之间的协作机器学习。但是，由于模型聚合策略，现有的框架需要严格的模型同质性，从而在更复杂的情况下限制了应用程序。此外，FL模型和梯度传输的通信成本极高。本文提出了松散耦合的联合学习（LC-FL），该框架使用生成模型作为传输媒体，以实现低通信成本和异质的联合学习。 LC-FL可以应用于客户具有不同类型的机器学习模型的方案。涵盖不同多部分场景的现实世界数据集的实验证明了我们的提议的有效性。
Federated learning (FL) was proposed to achieve collaborative machine learning among various clients without uploading private data. However, due to model aggregation strategies, existing frameworks require strict model homogeneity, limiting the application in more complicated scenarios. Besides, the communication cost of FL's model and gradient transmission is extremely high. This paper proposes Loosely Coupled Federated Learning (LC-FL), a framework using generative models as transmission media to achieve low communication cost and heterogeneous federated learning. LC-FL can be applied on scenarios where clients possess different kinds of machine learning models. Experiments on real-world datasets covering different multiparty scenarios demonstrate the effectiveness of our proposal.
