Phyelds: A Pythonic Framework for Aggregate Computing
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912992684146688 |
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| author | Aguzzi, Gianluca Domini, Davide Farabegoli, Nicolas Viroli, Mirko |
| author_facet | Aguzzi, Gianluca Domini, Davide Farabegoli, Nicolas Viroli, Mirko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29999 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Phyelds: A Pythonic Framework for Aggregate Computing Aguzzi, Gianluca Domini, Davide Farabegoli, Nicolas Viroli, Mirko Software Engineering Artificial Intelligence Programming Languages 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. |
| title | Phyelds: A Pythonic Framework for Aggregate Computing |
| topic | Software Engineering Artificial Intelligence Programming Languages |
| url | https://arxiv.org/abs/2603.29999 |