Phyelds: A Pythonic Framework for Aggregate Computing

TL;DR

Phyelds is a Python library that enables aggregate computing with a Pythonic API, facilitating integration with machine learning and supporting diverse applications like federated learning and multi-agent systems.

Contribution

It introduces Phyelds, a lightweight, Pythonic framework for aggregate programming that bridges the gap between aggregate computing and data science practitioners.

Findings

Supports a wide range of aggregate computing patterns

Enables integration with machine learning ecosystems

Demonstrates versatility in domains like federated learning

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.