Decentralised and collaborative machine learning framework for IoT (2312.12190v1)

Abstract: Decentralised machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralised and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.

• The paper demonstrates a decentralized ML framework that uses an adapted ILVQ algorithm for effective prototype sharing among IoT devices.
• The paper finds that a sharing frequency between 0.05 and 0.2 optimizes model accuracy and efficiency while reducing network communication overhead.
• The paper shows that both random and relative threshold protocols can successfully disseminate local models, supporting collaborative learning in resource-constrained environments.

A Comparative Analysis of Data Sharing Protocols in Decentralized Machine Learning for IoT Applications

Introduction

Decentralized Machine Learning (ML) has emerged as a solution to the inherent security and privacy issues present in traditional federated learning approaches, particularly for Internet of Things (IoT) environments characterized by resource-constrained devices. This paper introduces a comprehensive framework for decentralized and collaborative machine learning, specifically designed for IoT contexts. It leverages an adapted Incremental Learning Vector Quantization (ILVQ) algorithm alongside novel protocols for model sharing among computing elements in a network. The paper contrasts two distinct data sharing protocols—random sharing and relative threshold sharing—to examine their effects on ML model performance within decentralized architectures.

Decentralized Machine Learning Framework

The proposed framework includes several key components:

• An incremental learning algorithm adjusted for low-performance devices, named as the adaptation of ILVQ (XuILVQ).
• Two sharing protocols designed to facilitate decentralized collaboration: a random sharing protocol and a relative threshold protocol. These protocols are aimed at optimizing the sharing of local models (prototypes) among nodes to enrich individual models with global knowledge.
• The combination of these components enables a novel approach where devices work collaboratively to enhance ML model accuracy, efficiency, and robustness in distributed environments without relying on a central aggregator or exposing individual data points.

Methodology and Implementation

The experimental setup simulated a network of nodes, each running an instance of the proposed ML model on local datasets. The research focused on how different sharing frequencies, dictated by the sharing protocols, impact model performance—specifically concerning accuracy (F1 score), convergence time, memory usage, and communication overhead. By comparing the performance of decentralized approaches against a centralized benchmark, the paper aimed to determine the efficacy of prototype sharing in improving learning outcomes across networked IoT devices.

Findings and Insights

Analysis reveals several key findings:

1. Optimal Sharing Frequency: A certain level of prototype sharing between 0.05 and 0.2 significantly boosts model performance. Beyond this range, additional sharing offers diminishing returns on model improvement while increasing network load.
2. Impact of Sharing Protocols: The choice of sharing protocol (random vs. relative threshold) had a minimal effect on performance metrics in the simulated homogeneous network environment. This suggests that even simple sharing mechanisms can effectively disseminate knowledge across nodes, underlining the importance of the sharing strategy over its complexity in certain network architectures.
3. Trade-offs and Considerations: The paper highlighted a trade-off between model performance and network communication overhead. Balancing these factors is crucial in resource-constrained environments like IoT, where network efficiency and conservation of computational resources are paramount.

Theoretical and Practical Implications

Theoretically, this research enriches the discourse on decentralized learning by demonstrating the practical viability of prototype-based learning combined with simple data sharing protocols in distributed settings. Practically, it offers a viable framework for IoT applications, emphasizing the adaptability and robustness of ML models in decentralized and dynamic environments.

Future Directions

This paper opens several avenues for future research, including:

• Exploration of more sophisticated data sharing protocols, potentially dynamic or context-aware, to optimize learning efficiency further.
• Extending the architecture to more heterogeneous network environments, possibly incorporating nodes with varying capacities and constraints.
• Investigating the security implications of decentralized learning in IoT, focusing on the integrity and confidentiality of shared prototypes.

Conclusion

The comparative analysis of data sharing protocols within a decentralized ML framework presents a promising avenue for enhancing collaborative learning in IoT environments. The findings underscore the significance of judiciously selecting sharing parameters and protocols to maximize learning efficiency while minimizing network strain. As IoT networks grow in complexity and scale, such decentralized learning frameworks, empowered by simple yet effective sharing mechanisms, could play a crucial role in harnessing the collective intelligence of distributed devices.