Phyelds: A Pythonic Framework for Aggregate Computing
This work addresses the gap for data science practitioners who predominantly use Python, enabling easier adoption of aggregate computing in machine learning applications, though it is incremental as it adapts existing paradigms to a new language.
The authors tackled the lack of a Python-based aggregate programming framework for data science practitioners by developing Phyelds, a Python library that implements field calculus and integrates with Python's machine learning ecosystem, demonstrating its versatility across domains like federated learning and multi-agent reinforcement learning.
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.