May 1, 2024 · Based on simulation and experimental results, our approach outperforms baseline methods by increasing trust levels, global model accuracy, and ...

May 1, 2024 · Based on simulation and experimental results, our approach outperforms baseline methods by increasing trust levels, global model accuracy, and ...

Oct 29, 2024 · Based on simulation and experimental results, our approach outperforms baseline methods by increasing trust levels, global model accuracy, and ...

This paper presents a framework for enhancing mutual trustworthiness in federated learning for smart cities. It combines game theory, reputation systems, and ...

Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities. IEEE Internet Things J. Pub Date : 2024-10-09. DOI : 10.1109/jiot ...

May 2, 2024 · Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities. https://arxiv.org/abs/2405.00394 · 5:38 AM · May 2, 2024.

Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities ; Institute of Electrical and Electronics Engineers · 10.1109/jiot.2024.3476950.

Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities. O Wehbi, S Arisdakessian, M Guizani, OA Wahab, A Mourad, H Otrok ...

In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data.

Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities ... Federated learning is a promising collaborative and privacy-preserving ...