Phyelds: A Pythonic Framework for Aggregate Computing

• URL: http://arxiv.org/abs/2603.29999v1
• Date: Tue, 31 Mar 2026 16:57:32 GMT
• Title: Phyelds: A Pythonic Framework for Aggregate Computing
• Abstract summary: Phyelds is a Python library for aggregate programming.<n>It offers an implementation of the field model of calculus, featuring a Pythonic API and an architecture designed for seamless integration with Python's machine learning ecosystem.
• Score: 0.6719751155411074
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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