Federated learning is an approach to train machine learning models on the edge of the networks, as close as possible where the data is produced, motivated by the emerging problem of the inability to stream and centrally store the large amount of data produced by edge devices as well as by data privacy concerns. This learning paradigm is in need of robust algorithms to device heterogeneity and data heterogeneity. This paper proposes ModFL as a federated learning framework that splits the models into a configuration module and an operation module enabling federated learning of the individual modules. This modular approach makes it possible to extract knowlege from a group of heterogeneous devices as well as from non-IID data produced from its users. This approach can be viewed as an extension of the federated learning with personalisation layers FedPer framework that addresses data heterogeneity. We show that ModFL outperforms FedPer for non-IID data partitions of CIFAR-10 and STL-10 using CNNs. Our results on time-series data with HAPT, RWHAR, and WISDM datasets using RNNs remain inconclusive, we argue that the chosen datasets do not highlight the advantages of ModFL, but in the worst case scenario it performs as well as FedPer.

翻译：联邦学习是在网络边缘培训机器学习模型的一种方法,在产生数据的地方尽可能靠近数据的地方培训机器学习模型,其动机是无法流出和集中储存边缘设备产生的大量数据这一新出现的问题以及数据隐私问题。这种学习范式需要强有力的算法,以装置异质性和数据异质性。本文建议MadFL作为一个联合学习框架,将模型分为一个配置模块和一个操作模块,使每个模块能够进行联合学习。这种模块化方法使得有可能从一组混杂设备以及用户产生的非IID数据中提取知识腿。这一方法可以被视为个人化的FedPer框架的扩展,该框架处理数据异质性。我们表明,MadFL在非IID数据分区方面,MFAR-10和STL-10使用CNIS-10,比FD的FD的FedPer优。我们用HAPT、RWHARHAR和WISDDD数据集的时间序列数据分析结果显示,使用RNFP的FD的FD没有确定其最坏的优势。