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.

翻译：随着数据隐私问题的日益关注，机器学习架构正从集中式向分布式范式转变，催生出联邦学习和分割学习作为两种主流的隐私保护机器学习机制。然而，在支持设备到设备通信的异构网络中部署联邦学习或分割学习面临显著挑战，包括架构可扩展性不足及训练延迟过长。针对这些问题，本文提出两种创新的混合分布式机器学习架构——混合分割联邦学习与混合联邦分割学习。这些架构在支持D2D通信的异构无线网络中融合了联邦学习与分割学习的优势。我们系统分析了HSFL与HFSL的性能优势，同时指出了未来探索中的开放挑战。通过在非独立同分布场景下使用三个数据集进行的初步仿真验证了所提架构的可行性。仿真结果表明，相较于传统联邦学习与分割学习，本方案在通信/计算开销及训练延迟方面均实现了显著降低。