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 processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on x86-64 and ARM platforms and an emerging RISC-V one. 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）无需集中式输入数据即可实现协作式机器学习，联邦学习（FL）和边缘推理是其典型范例。尽管面向DML（尤其是联邦学习）的工具正蓬勃兴起，但多数工具在灵活性及可移植性方面仍存在不足，难以支持新型处理器（如RISC-V）、非全连接网络拓扑及异步协作方案的实验研究。我们通过设计一种领域专用语言克服上述局限，该语言可将DML方案映射至底层中间件，即FastFlow并行编程库。我们在x86-64平台、ARM平台及新兴RISC-V平台上生成多种可运行的DML方案进行实验验证，并对所提方案及系统的性能与能效进行量化表征。作为研究副产品，我们完成了PyTorch框架向RISC-V平台的移植工作——据我们所知，这是首个公开可用的RISC-V移植版本。