We present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities.
To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated ...
Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach ...
Towards Mutual Trust-Based Matching For Federated Learning Client Selection ; 2023 International Wireless Communications and Mobile Computing (IWCMC 2023).
المؤلفون المشاركون ; Towards Mutual Trust-Based Matching For Federated Learning Client Selection. O Wehbi, OA Wahab, A Mourad, H Otrok, H Alkhzaimi, M Guizani.
May 1, 2024 · Our approach entails: (1) Creating preference functions for servers and clients, allowing them to rank each other based on trust scores, (2) ...
Our approach entails: (1) Creating preference functions for servers and clients, allowing them to rank each other based on trust scores, (2) Establishing a ...
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PPEFL: An Edge Federated Learning Architecture with Privacy-Preserving Mechanism · Towards Mutual Trust-Based Matching For Federated Learning Client Selection.
Federated learning is defined as one of the solutions that use distributed clients to train and aggregate the target model without sharing the private ...
Towards Mutual Trust-Based Matching For Federated Learning Client Selection ... Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing.