Lightweight and Dropout Toleration Aggregation for Privacy Crowdsourcing Federated Learning

Xiaodi Wang, Yunwei Dong, Meng Li, Yining Liu

Abstract: Federated learning-based mobile crowdsourcing (F-MCS) leverages crowdsourcing for large-scale data perception, but it faces challenges from privacy concerns and network instability problems. Hence, privacy-protecting F-MCS schemes have been proposed to address these issues by aggregating local models on a trusted central server or a trusted third party (TTP). However, these schemes are still vulnerable to single points of failure and other malicious attacks, making them impractical. Moreover, due to the instability of the communication network, workers in the F-MCS scheme may drop out of the task, which oversees the entire model aggregation. In order to tackle the obstacles above, we design an aggregation method combined with Shamir secret sharing that comes with secure aggregation of global models without relying on a TTP. In addition, to enhance the robustness and adaptability of the scheme, we handle worker disconnection and new user joining to maintain protocol continuity and data integrity, thus tolerating dropouts and dynamic participation. We have conducted a thorough analysis of the scheme’s security, which shows that it can effectively protect user data privacy. Furthermore, our experimental results demonstrate that the proposed scheme performs well in model accuracy and is comparable to the system performance in the nondropout case.

External IDs:dblp:journals/tii/WangDLL25