Search Results for author: Mohamed Abdallah

Found 16 papers, 1 papers with code

Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approach

no code implementations19 Jan 2024 Moqbel Hamood, Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha

Clustered Federated Multitask Learning (CFL) has gained considerable attention as an effective strategy for overcoming statistical challenges, particularly when dealing with non independent and identically distributed (non IID) data across multiple users.

The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey

no code implementations11 Jan 2024 Nima Abdi, Abdullatif Albaseer, Mohamed Abdallah

{ This is followed by a critical discussion on their practical implications and broader impact on cybersecurity in Smart Grids.}

Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally

no code implementations5 Oct 2023 Shawqi Al-Maliki, Adnan Qayyum, Hassan Ali, Mohamed Abdallah, Junaid Qadir, Dinh Thai Hoang, Dusit Niyato, Ala Al-Fuqaha

This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating pro-social applications.

Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets

no code implementations28 Feb 2022 Abdulmalik Alwarafy, Bekir Sait Ciftler, Mohamed Abdallah, Mounir Hamdi, Naofal Al-Dhahir

This paper considers the problem of cost-aware downlink sum-rate maximization via joint optimal radio access technologies (RATs) assignment and power allocation in next-generation heterogeneous wireless networks (HetNets).

Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks

no code implementations16 Aug 2021 Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha, Aiman Erbad

Extensive experiments show that the proposed approach lowers the training time and accelerates the convergence rate by up to 50% while imbuing each client with a specialized model that is fit for its local data distribution.

Federated Learning Scheduling

Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey

no code implementations13 Jul 2021 Marwan Dhuheir, Abdullatif Albaseer, Emna Baccour, Aiman Erbad, Mohamed Abdallah, Mounir Hamdi

Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements.

Emotion Recognition

Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning

no code implementations20 Jun 2021 Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha, Aiman Erbad

Specifically, we consider a problem that aims to find the optimal user's resources, including the fine-grained selection of relevant training samples, bandwidth, transmission power, beamforming weights, and processing speed with the goal of minimizing the total energy consumption given a deadline constraint on the communication rounds of FEEL.

Total Energy

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

no code implementations25 May 2021 Abdulmalik Alwarafy, Mohamed Abdallah, Bekir Sait Ciftler, Ala Al-Fuqaha, Mounir Hamdi

In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks.

Management

Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA

no code implementations3 Apr 2021 Muhammad Shehab, Bekir S. Ciftler, Tamer Khattab, Mohamed Abdallah, Daniele Trinchero

In this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) scenario with the aim of maximizing the sum rate of users.

reinforcement-learning Reinforcement Learning (RL)

Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning

no code implementations30 Mar 2021 Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha, Aiman Erbad

Then, the problem is formulated as joint energy minimization and resource allocation optimization problem to obtain the optimal local computation time and the optimal transmission time that minimize the total energy consumption considering the worker's energy budget, available bandwidth, channel states, beamforming, and local CPU speed.

Total Energy

Local Bitcoin Network Simulator for Performance Evaluation using Lightweight Virtualization

1 code implementation4 Feb 2020 Lina Alsahan, Noureddine Lasla, Mohamed Abdallah

This paper presents a new blockchain network simulator that uses bitcoin's original reference implementation as its main application.

Cryptography and Security

Exploiting Unlabeled Data in Smart Cities using Federated Learning

no code implementations10 Jan 2020 Abdullatif Albaseer, Bekir Sait Ciftler, Mohamed Abdallah, Ala Al-Fuqaha

The algorithm is divided into two phases where the first phase trains a global model based on the labeled data.

Federated Learning

Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method

no code implementations7 Jan 2020 Bekir Sait Ciftler, Abdullatif Albaseer, Noureddine Lasla, Mohamed Abdallah

Although crowdsourcing is an excellent way to gather immense amounts of data, it jeopardizes the privacy of participants, as it requires to collect labeled data at a centralized server.

Federated Learning Privacy Preserving

On Sharing Models Instead of Data using Mimic learning for Smart Health Applications

no code implementations24 Dec 2019 Mohamed Baza, Andrew Salazar, Mohamed Mahmoud, Mohamed Abdallah, Kemal Akkaya

In this paper, we tackle this problem by sharing the models instead of the original sensitive data by using the mimic learning approach.

BIG-bench Machine Learning

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