PPSFL: Privacy-Preserving Split Federated Learning for heterogeneous data in edge-based Internet of Things

Abstract

With the rapid increase in the number of Internet of Things (IoT) devices and the amount of data they generate, the traditional cloud-based approach is gradually unable to meet the actual needs of many scenarios. Distributed collaborative machine learning (DCML) paradigms such as Federated Learning (FL) and Split Learning (SL) provide possibilities for effective use of decentralized data in edge-based IoT. However, critical challenges in terms of data privacy, heterogeneity, and constrained resources remain to be handled. Despite extensive efforts, current solutions still cannot address the above challenges simultaneously. Therefore, studies in this emerging research field remain inadequate. In this paper, we propose a hybrid framework for combining FL with SL, named Privacy-Preserving Split Federated Learning (PPSFL). It facilitates privacy protection with an appropriate model decomposition strategy and mitigates the negative impact of data heterogeneity by incorporating private Group Normalization (GN) layers into the network. Extensive empirical results demonstrate that PPSFL attains better performance than other state-of-the-art distributed collaborative learning methods on different datasets. We also evaluate and compare the resistance of all baselines to reconstruction attacks with various image datasets. Results supported by comparative experiments indicate that our method can greatly prevent information leakage from raw data while maintaining classification performance. Additionally, the comparisons in terms of communication and computation overhead show that PPSFL is also competitive.