Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning

• URL: http://arxiv.org/abs/2501.04817v1
• Date: Wed, 08 Jan 2025 20:14:07 GMT
• Title: Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning
• Authors: Ziyuan Bao, Eiman Kanjo, Soumya Banerjee, Hasib-Al Rashid, Tinoosh Mohsenin,
• Abstract summary: This paper proposes a novel framework, bilayer Gossip Decentralised Parallel Descent (GDD) GDD addresses intermittent connectivity, limited communication range, and dynamic network topologies. We evaluate the framework's performance against the Centralised Federated Learning (CFL) baseline.
• Score: 2.6913398550088483
• License:
• Abstract: With the growing computational capabilities of microcontroller units (MCUs), edge devices can now support machine learning models. However, deploying decentralised federated learning (DFL) on such devices presents key challenges, including intermittent connectivity, limited communication range, and dynamic network topologies. This paper proposes a novel framework, bilayer Gossip Decentralised Parallel Stochastic Gradient Descent (GD PSGD), designed to address these issues in resource-constrained environments. The framework incorporates a hierarchical communication structure using Distributed Kmeans (DKmeans) clustering for geographic grouping and a gossip protocol for efficient model aggregation across two layers: intra-cluster and inter-cluster. We evaluate the framework's performance against the Centralised Federated Learning (CFL) baseline using the MCUNet model on the CIFAR-10 dataset under IID and Non-IID conditions. Results demonstrate that the proposed method achieves comparable accuracy to CFL on IID datasets, requiring only 1.8 additional rounds for convergence. On Non-IID datasets, the accuracy loss remains under 8\% for moderate data imbalance. These findings highlight the framework's potential to support scalable and privacy-preserving learning on edge devices with minimal performance trade-offs.

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