We build a trust framework between the server and its available clients. The trust factor is continuously monitored and updated by checking clients that are ...
May 1, 2024 · In our research, we are motivated to integrate a trust element into the client selection and model deployment processes within our system ...
However, doubts arise about the trustworthiness of devices deployed as clients in FL scenarios, especially when container deployment processes are involved.
The proposed on-demand deployment takes into consideration the trust factor of a client while deploying them in the learning by greedily selecting clients with ...
The authors of [21] investigated the client trustworthiness problems for federated learning, where the trust factor is continuously updated after the client has ...
Co-authors ; Towards trust driven on-demand client deployment in federated learning. M Chahoud, A Mourad, H Otrok, M Guizani. IEEE INFOCOM 2023-IEEE Conference ...
May 1, 2024 · Embracing containers for real-time deployment of learning algorithms unlocks new avenues for personalized services and adaptive decision-making.
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Dec 9, 2024 · In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in FL offering more volume and ...
Aug 7, 2024 · In this paper, we propose a trustworthy FL method incorporating Q-learning, trust, and reputation mechanisms, enhancing model accuracy and fairness.
May 13, 2024 · This research paper explores an on-demand model and client deployment approach for federated learning using deep reinforcement learning.