[2603.29999] Phyelds: A Pythonic Framework for Aggregate Computing

Computer Science > Software Engineering arXiv:2603.29999 (cs) [Submitted on 31 Mar 2026] Title:Phyelds: A Pythonic Framework for Aggregate Computing Authors:Gianluca Aguzzi, Davide Domini, Nicolas Farabegoli, Mirko Viroli View a PDF of the paper titled Phyelds: A Pythonic Framework for Aggregate Computing, by Gianluca Aguzzi and 3 other authors View PDF HTML (experimental) 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 desc...