A Unified Blockchain-based Framework for Decentralized Collaborative Transfer Learning using Adaptive Incentivization

Transfer learning, a method that influences pretrained models to improve performance on new tasks, faces several data privacy, security, and scalability challenges when applied in decentralized environments. Recently, there have been few attempts to integrate blockchain technology with a transfer-learning approach. However, these classical blockchainbased non-collaborative transfer learning models face significant challenges, including poor scalability, security vulnerability, lack of data diversity, and inefficient incentive structures, thereby hindering the effectiveness and efficiency of the transfer learning process. This paper presents a unified framework that utilizes decentralized blockchain technology for collaborative transfer learning. The framework enables secure model sharing within a collaborative learning environment, supported by incentive mechanisms while maintaining model integrity and providing context protection. We perform extensive experimental studies with eight well-known large-scale and fine-grained image datasets. After initial transfer learning, incorporating collaborative learning via the decentralized blockchain network yielded promising results. The global model, after aggregating contributions from all entities, shows an impressive 6.8 % to 10.0 % accuracy improvement compared to individual baseline models. For blockchain implementation, we use the Rahasak blockchain as the ledger and its Aplos platform for a customized smart contract interface. Rahasak-CA, the certificate authority of the Rahasak blockchain, stores the digital certificates of peers in the transfer learning process. The system was deployed using Docker and Kubernetes, and evaluated on the proposed testbed.