Blockchain-enabled decentralized machine learning for reliable and collaborative edge intelligence

Artificial intelligence (AI) is a key enabler of the sixth-generation (6G) mobile communication systems. Traditional AI infrastructures rely on cloud services, leading to high costs and data security concerns. To address the limitations of AI centralization, this thesis explores blockchain, a distributed ledger technology, to decentralize AI to the 6G network edge. To improve the quality of service (QoS) in edge intelligence systems, we optimize the training and inference processes, considering unique challenges such as disparate data quality, resource limitations, and data distribution shifts. First, we investigate a collaborative decentralized machine learning (DML) approach. For the first time, blockchain is used as a time-dependent incentive mechanism with a diversified reward model. Instead of fine-tuning AI models, we focus on assessing and conditioning data at the 6G edge. A decentralized data valuation protocol is introduced atop blockchain to incentivize contributors and disincentivize free-riders. Simulation results show that optimizing information loss and the marginal utility of individual data samples is key to improving QoS in collaborative edge intelligence systems. Second, we focus on blockchain-enabled DML with enhanced security and reduced latency. We propose a novel proof-of-useful-work protocol to utilize edge computing resources efficiently. Moreover, we apply mean-field theory and introduce a novel aggregation protocol named Corrected Krum to mitigate the negative impact of data poisoning attacks. To minimize per-round latency, the optimization problem is formulated as a multi-way number partitioning challenge. Extensive simulations demonstrate that our blockchain approach enhances reliability and QoS for edge AI systems in diverse data environments. Finally, we optimize edge AI deployment. To address data distribution shifts during model inference, a blockchain-based model selection approach is developed for test-time adaptation. Blockchain consensus is applied for the first time to deliver cost-effective inference for resource-constrained edge devices. Our blockchain approaches enable DML to adapt to data distribution shifts with low latency and superior prediction accuracy. Furthermore, we decentralize multi-modal large AI model inference to the 6G edge. An online bipartite matching protocol is proposed for rapid resource allocation during peak service hours. Experimental results confirm blockchain’s potential for reliable and collaborative edge intelligence with improved QoS.