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Phyelds: A Pythonic Framework for Aggregate Computing

Gianluca Aguzzi, Davide Domini, Nicolas Farabegoli, Mirko Viroli

Abstract

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.

Phyelds: A Pythonic Framework for Aggregate Computing

Abstract

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.

Paper Structure

This paper contains 16 sections, 2 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Aggregate Programming - A Brief Overview
  3. System Model
  4. Field Calculus Operators
  5. Alignment Mechanisms
  6. The Phyelds Library
  7. VM Module.
  8. Data Module.
  9. Calculus Module.
  10. Library Module.
  11. Simulator Module.
  12. Example of Use
  13. Common Aggregate Computing Patterns.
  14. Self-Organizing Federated Learning.
  15. Integration with Third-Party Simulators.
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Graphical representation of the aggregate computing system model.
  • Figure 2: Architecture of Phyelds. Modules (components) contain classes (rectangles), submodules (dashed borders), and functions (ovals). Arrows indicate dependencies.
  • Figure 3: Examples of common aggregate programming patterns implemented in Phyelds. On the left, a channel is constructed between a source device (bottom left) and a target device (top right). The channel color is yellow. On the right, devices are partitioned into coordination regions (different colors).
  • Figure 4: Vicsek flocking simulation in VMAS using Phyelds. Agents (cirlces) align their velocities over time (left to right), exhibiting coherent collective motion.