Phyelds: A Pythonic Framework for Aggregate Computing
2026-03-31 • Software Engineering
Software EngineeringArtificial IntelligenceProgramming Languages
AI summaryⓘ
The authors present Phyelds, a new Python library that allows programmers to use aggregate programming—a way to coordinate many devices at once—in the popular Python language. This is important because most data scientists and machine learning experts use Python, but previous tools for aggregate programming weren't made for Python users. Phyelds is designed to work smoothly with Python’s machine learning tools and can be used in areas like robotics and distributed learning. The authors show how Phyelds can handle different tasks, from classic programming patterns to advanced learning simulations.
aggregate programmingfield calculusPythonmachine learningdistributed learningfederated learningmulti-agent reinforcement learningroboticsIoTdata science
Authors
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.