# Phyelds: A Pythonic Framework for Aggregate Computing

arXiv: https://arxiv.org/abs/2603.29999 · HTML: https://mlsanity.com/paper/2603.29999/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [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)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning](https://mlsanity.com/paper/2207.03530/) (2022)
- [Macroprogramming: Concepts, State of the Art, and Opportunities of Macroscopic Behaviour Modelling](https://mlsanity.com/paper/2201.03473/) (2022)
- [A Higher-order Calculus of Computational Fields](https://mlsanity.com/paper/1610.08116/) (2016)
- [FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2502.08577/) (2025)
- [ProFed: a Benchmark for Proximity-based non-IID Federated Learning](https://mlsanity.com/paper/2503.20618/) (2025)
- [Software Engineering for Collective Cyber-Physical Ecosystems](https://mlsanity.com/paper/2406.04780/) (2024)
- [Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0](https://mlsanity.com/paper/2507.07613/) (2025)

## Built on by (most cited first, 1 of 1)

- [C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift](https://mlsanity.com/paper/2606.18003/) (2026)

## Abstract (arXiv)

Aggregate programming is a field-based coordination paradigm with over a decade of exploration and successful applications across domains including sensor networks, robotics, and IoT, with implementations in various programming languages, such as Protelis, ScaFi (Scala), and FCPP (C++). A recent research direction integrates machine learning with aggregate computing, aiming to support large-scale distributed learning and provide new abstractions for implementing learning algorithms. However, existing implementations do not target data science practitioners, who predominantly work in Python--the de facto language for data science and machine learning, with a rich and mature ecosystem. Python also offers advantages for other use cases, such as education and robotics (e.g., via ROS). To address this gap, we present Phyelds, a Python library for aggregate programming. Phyelds offers a fully featured yet lightweight implementation of the field calculus model of computation, featuring a Pythonic API and an architecture designed for seamless integration with Python's machine learning ecosystem. We describe the design and implementation of Phyelds and illustrate its versatility across domains, from well-known aggregate computing patterns to federated learning coordination and integration with a widely used multi-agent reinforcement learning simulator.