Distributed Machine Learning in D2D-Enabled Heterogeneous Networks: Architectures, Performance, and Open Challenges

TL;DR

This paper proposes hybrid distributed machine learning architectures for D2D-enabled heterogeneous networks, addressing privacy, scalability, and delay challenges, with simulations showing improved efficiency over traditional methods.

Contribution

Introduction of hybrid split FL and hybrid federated SL architectures that combine FL and SL for better performance in D2D networks.

Findings

Reduced communication and computation costs.

Lower training delays.

Feasibility demonstrated through preliminary simulations.

Abstract

The ever-growing concerns regarding data privacy have led to a paradigm shift in machine learning (ML) architectures from centralized to distributed approaches, giving rise to federated learning (FL) and split learning (SL) as the two predominant privacy-preserving ML mechanisms. However,implementing FL or SL in device-to-device (D2D)-enabled heterogeneous networks with diverse clients presents substantial challenges, including architecture scalability and prolonged training delays. To address these challenges, this article introduces two innovative hybrid distributed ML architectures, namely, hybrid split FL (HSFL) and hybrid federated SL (HFSL). Such architectures combine the strengths of both FL and SL in D2D-enabled heterogeneous wireless networks. We provide a comprehensive analysis of the performance and advantages of HSFL and HFSL, while also highlighting open challenges for future exploration. We support our proposals with preliminary simulations using three datasets in non-independent and non-identically distributed settings, demonstrating the feasibility of our architectures. Our simulations reveal notable reductions in communication/computation costs and training delays as compared to conventional FL and SL.