# Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning

arXiv: https://arxiv.org/abs/2302.07946 · HTML: https://mlsanity.com/paper/2302.07946/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [OpenFL: An open-source framework for Federated Learning](https://mlsanity.com/paper/2105.06413/) (2021)
- [Fathom: Reference Workloads for Modern Deep Learning Methods](https://mlsanity.com/paper/1608.06581/) (2016)
- [Monte Cimone: Paving the Road for the First Generation of RISC-V High-Performance Computers](https://mlsanity.com/paper/2205.03725/) (2022)

## Built on by (most cited first, 3 of 3)

- [Fedstellar: A Platform for Decentralized Federated Learning](https://mlsanity.com/paper/2306.09750/) (2023)
- [Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU](https://mlsanity.com/paper/2504.03774/) (2025)
- [Efficiently Distributed Federated Learning](https://mlsanity.com/paper/2609.19972/) (2026)

## Abstract (arXiv)

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.