A Hybrid Clustered Approach for Enhanced Communication and Model Performance in Blockchain-Based Collaborative Learning

Abstract

Collaborative edge learning has emerged in various domains like vehicular networks and medical care, allowing local model training on edge devices while preserving privacy. The integration of blockchain technology further enhances security and privacy in the learning environment, although challenges such as communication overhead and vulnerability to poisoning attacks have yet to be resolved. This paper proposes a hybrid clustered blockchain method (HCB) for collaborative edge learning, which introduces a unique combination of cluster-based model updates and the use of delegate nodes for efficient model aggregation. We introduce the delegate-based adaptive model aggregation for robust collaborative learning, termed DAMA-RCL, and a novel disassembling-reassembling method for practical model transmission on the blockchain network. Experimental results demonstrate that HCB significantly enhances communication efficiency, reducing hop counts and transmission time by over 90%, while maintaining learning performance compared to traditional collaborative learning. Additionally, DAMA-RCL exhibits strong resilience in scenarios with up to 50% malicious clients. The HCB approach for collaborative edge learning, along with the DAMA-RCL algorithm and the model disassembling-reassembling method, provides a promising solution to the challenges of communication efficiency, privacy, and security, paving the way for effective and reliable collaborative learning in various application domains.

Keywords

• Blockchain-based
• collaborative edge learning
• privacy preservation
• communication efficiency
• robustness