13 citations · 23 across the 7 of their papers we have counts for
7 papers
Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data
Shadha Tabatabai, Ihab Mohammed, Basheer Qolomany +4
Federated Learning (FL) is one of the hot research topics, and it utilizes Machine Learning (ML) in a distributed manner without directly accessing private data on clients. However…
Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks
Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha +1
Clustered Federated Multitask Learning (CFL) was introduced as an efficient scheme to obtain reliable specialized models when data is imbalanced and distributed in a non-i.i.d. (no…
Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey
Marwan Dhuheir, Abdullatif Albaseer, Emna Baccour +3
Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements. Automatically identifying the emot…
Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning
Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha +1
In Federated edge learning (FEEL), energy-constrained devices at the network edge consume significant energy when training and uploading their local machine learning models, leadin…
Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning
Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha +1
Federated edge learning (FEEL) is a promising distributed learning technique for next-generation wireless networks. FEEL preserves the user's privacy, reduces the communication cos…
Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method
Bekir Sait Ciftler, Abdullatif Albaseer, Noureddine Lasla +1
Received Signal Strength (RSS) fingerprint-based localization has attracted a lot of research effort and cultivated many commercial applications of location-based services due to i…