Blockchain-Based Privacy-Preserving Federated Learning for Mobile Crowdsourcing.

Mobile crowdsourcing (MCS) is an emerging paradigm that enables the outsourcing of a complex task to a group of mobile devices. The ability to utilize the collective power of mobile devices and human intelligence makes MCS a significant tool in various scenarios. Nevertheless, it faces practical challenge in protecting user privacy due to the sensitive nature of information collected by mobile devices. Additionally, the inherent openness of MSC and the heterogeneity of mobile devices raise reliability concerns among participants. To address these challenges, by integrating Federated Learning with the pairwise additive masking technique and the Chinese Remainder Theorem, we propose a Blockchain-based Privacy-preserving Federated Learning (BPFL) framework for mobile crowdsourcing, which allows mobile participants to collaboratively solve a crowdsourced machine learning task while preserving privacy. Besides, it employs blockchain technology to record the training process in a transparent and tamper-proof ledger. This ledger guarantees the verifiability of aggregation results and the fair distribution of training rewards, thereby enhancing trust and fairness. We prove that our BPFL supports privacy protection and trust mechanism simultaneously and resists inference and collusion attacks. Experimental results show that our BPFL can achieve high performance in terms of computation cost, communication cost and model accuracy, which is friendly for mobile users with resource-constrained devices in MCS ecosystems.