MicroPython Testbed for Federated Learning Algorithms (2405.09423v2)

Abstract: Recently, Python Testbed for Federated Learning Algorithms emerged as a low code and generative LLMs amenable framework for developing decentralized and distributed applications, primarily targeting edge systems, by nonprofessional programmers with the help of emerging artificial intelligence tools. This light framework is written in pure Python to be easy to install and to fit into a small IoT memory. It supports formally verified generic centralized and decentralized federated learning algorithms, as well as the peer-to-peer data exchange used in time division multiplexing communication, and its current main limitation is that all the application instances can run only on a single PC. This paper presents the MicroPyton Testbed for Federated Learning Algorithms, the new framework that overcomes its predecessor's limitation such that individual application instances may run on different network nodes like PCs and IoTs, primarily in edge systems. The new framework carries on the pure Python ideal, is based on asynchronous I/O abstractions, and runs on MicroPython, and therefore is a great match for IoTs and devices in edge systems. The new framework was experimentally validated on a wireless network comprising PCs and Raspberry Pi Pico W boards, by using application examples originally developed for the predecessor framework.

• The paper demonstrates that MPT-FLA extends its predecessor to run federated learning across distributed nodes, including IoT devices.
• The framework leverages MicroPython and asynchronous I/O to manage concurrent operations without traditional multiprocessing constraints.
• Practical experiments validate MPT-FLA’s efficiency in sensor data averaging, decentralized model convergence, and time synchronization for edge systems.

MicroPython Testbed for Federated Learning: A New Approach for Edge Systems

Introduction

In a fascinating development for federated learning, researchers have introduced the MicroPython Testbed for Federated Learning Algorithms (MPT-FLA). This framework extends a previous model and addresses its significant limitation—running all instances on a single PC. This new version opens doors for applications across network nodes, such as PCs and Internet of Things (IoT) devices, particularly in edge systems.

Design and Evolution

MPT-FLA builds on its predecessor, PTB-FLA, but introduces several critical advancements:

• MicroPython Compatibility: The framework runs on MicroPython, making it suitable for lightweight IoT devices with limited memory and processing power.
• Asynchronous I/O: Utilizing Python's asyncio, this framework simplifies concurrent operations without traditional multi-threading—an essential step given MicroPython's lack of complete multiprocessing support.

Why This Matters: Traditional FL frameworks like TensorFlow Federated and BlueFog are not optimized for edge-only deployments, particularly those requiring simple installation and minimal dependencies. MPT-FLA bridges this gap with a pure Python implementation suited for decentralized intelligent systems.

Experimental Validation

The authors validated MPT-FLA using a WiFi network consisting of PCs and Raspberry Pi Pico W boards. They adapted several algorithms from PTB-FLA, focusing primarily on functional correctness. The validation results were promising, showing the new framework produced the same numerical results as the previous one despite running across various network nodes.

Key Algorithms Explained

The paper explores several adapted algorithms to illustrate MPT-FLA's capabilities:

1. Federated Map Example:
   • Goal: Averages sensor readings above a given threshold.
   • Technical Note: This algorithm highlights how real sensor data from IoT devices can be integrated and processed.
2. Centralized Data Averaging:
   • Goal: Averages client models to converge on a single model (e.g., an average value).
   • Iterations: The algorithm required multiple iterations to converge, but interestingly, fewer than its decentralized counterpart.
3. Decentralized Data Averaging:
   • Goal: Allows all nodes to participate equally in model averaging.
   • Fast Convergence: This method converges more quickly than the centralized approach, demonstrating an efficient use of resources in a peer-to-peer network.
4. Orbit Determination and Time Synchronization (ODTS):
   • Simulation: A simplified example simulating how satellites exchange and synchronize their orbital data.
   • Complexity: Real ODTS implementations would use more advanced techniques like Kalman filters, but this example validates basic peer data exchange in time slots.

Practical Implications and Future Directions

Immediate Use Cases:

• Smart Homes: Privacy-preserving federated learning.
• Factory Automation: High-resilience digitalization.
• Satellite Communication: Efficient orbit data synchronization.

By offering an easy-to-use, lightweight, and highly flexible framework, MPT-FLA provides a robust foundation for developing edge-specific federated learning applications.

Future Plans:

• Performance Metrics: While initial validations focused on functional correctness, future research might evaluate metrics like execution time, network latency, and energy consumption.
• Advanced Use Cases: Expanding to benchmarks and ML-based methods for comprehensive performance evaluation.

Conclusion

The new MPT-FLA framework successfully builds on the original PTB-FLA by supporting distributed applications across multiple nodes, an important advancement for federated learning in edge environments. Its lightweight, pure Python implementation paired with asynchronous I/O support makes it well-suited for IoT devices.

As it stands, MPT-FLA is a strong candidate for various future applications in decentralized intelligence, from smart homes to complex industrial setups. This tool will undoubtedly play a role in the ever-expanding landscape of federated learning and edge computing.