Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities

arXiv:2405.00394v1 Announce Type: cross
Abstract: Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system's integrity due to the possibility of malicious client selection. Primarily, the existing literature focuses on assessing the trustworthiness of clients, neglecting the …

abstract arxiv challenge cities client collaborative cs.gt cs.lg data environments federated learning machine machine learning privacy random smart smart cities training trustworthy type urban usage