Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the \ff parallel programming library. We experiment with it by generating different working DML schemes on two emerging architectures (ARM-v8, RISC-V) and the x86-64 platform. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.

翻译：去中心化机器学习（DML）无需集中输入数据即可实现协作式机器学习，联邦学习（FL）和边缘推理是DML的典型应用。尽管DML（尤其是FL）工具正蓬勃发展，但许多工具在应对新型系统（如RISC-V）、非全连接拓扑及异步协作方案时缺乏灵活性和可移植性。我们通过设计一种领域特定语言来克服这些限制，该语言能够将DML方案映射到底层中间件（即\ff并行编程库）。我们通过该语言在两种新兴架构（ARM-v8、RISC-V）及x86-64平台上生成了多种可运行的DML方案，表征了所提方案与系统的性能及能效。作为副产品，我们首次公开了PyTorch框架的RISC-V移植版本（据我们所知）。