Modular Federated Learning

TL;DR

ModFL introduces a modular federated learning framework that enhances learning from heterogeneous devices and non-IID data, outperforming existing methods like FedPer on image datasets, with inconclusive results on certain time-series data.

Contribution

This paper presents ModFL, a novel modular federated learning framework that enables effective learning from heterogeneous devices and non-IID data, extending the FedPer approach.

Findings

ModFL outperforms FedPer on CIFAR-10 and STL-10 datasets.

Results on time-series datasets are inconclusive, with ModFL performing as well as FedPer.

ModFL effectively handles data and device heterogeneity in federated learning.

Abstract

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.