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）无需集中化输入数据即可实现协作式机器学习，联邦学习（FL）和边缘推理是其中的典型代表。尽管DML（尤其是FL）工具正蓬勃发展，但多数工具在面向新型处理器（如RISC-V）、非全连接网络拓扑及异步协作方案时，缺乏足够的灵活性和可移植性。我们通过一种领域特定语言解决了上述局限，该语言可将DML方案映射至底层中间件——即FastFlow并行编程库。我们基于x86-64平台、ARM平台及新兴的RISC-V平台生成了不同运行中的DML方案并开展实验。通过对所构建方案及系统的性能与能效进行刻画，我们作为衍生成果推出了PyTorch框架的RISC-V移植版本——据我们所知，这是首个公开可用的此类移植成果。