Sawsan AbdulRahman

1.1k total citations · 1 hit paper
7 papers, 761 citations indexed

About

Sawsan AbdulRahman is a scholar working on Artificial Intelligence, Computer Science Applications and Computer Networks and Communications. According to data from OpenAlex, Sawsan AbdulRahman has authored 7 papers receiving a total of 761 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Computer Science Applications and 2 papers in Computer Networks and Communications. Recurrent topics in Sawsan AbdulRahman's work include Privacy-Preserving Technologies in Data (7 papers), Cryptography and Data Security (5 papers) and Mobile Crowdsensing and Crowdsourcing (3 papers). Sawsan AbdulRahman is often cited by papers focused on Privacy-Preserving Technologies in Data (7 papers), Cryptography and Data Security (5 papers) and Mobile Crowdsensing and Crowdsourcing (3 papers). Sawsan AbdulRahman collaborates with scholars based in Lebanon, United Arab Emirates and Canada. Sawsan AbdulRahman's co-authors include Azzam Mourad, Chamseddine Talhi, Hanine Tout, Hakima Ould‐Slimane, Mohsen Guizani, Safa Otoum and Ouns Bouachir and has published in prestigious journals such as IEEE Journal on Selected Areas in Communications, IEEE Internet of Things Journal and Vehicular Communications.

In The Last Decade

Sawsan AbdulRahman

6 papers receiving 741 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

2020 504 citations Sawsan AbdulRahman, Hanine Tout et al. IEEE Internet of Things Journal profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sawsan AbdulRahman Lebanon	4	619	218	155	125	112	7	761
Hakima Ould‐Slimane Canada	9	526 0.8×	214 1.0×	142 0.9×	139 1.1×	70 0.6×	32	709
Jed Mills United Kingdom	7	489 0.8×	224 1.0×	157 1.0×	123 1.0×	97 0.9×	8	650
Chandra Thapa Australia	11	622 1.0×	241 1.1×	126 0.8×	177 1.4×	86 0.8×	25	942
Qasim Ali Arain Pakistan	15	265 0.4×	191 0.9×	313 2.0×	115 0.9×	79 0.7×	37	676
Jer Shyuan Ng Singapore	9	447 0.7×	300 1.4×	184 1.2×	128 1.0×	98 0.9×	21	705
Yuze Zou China	12	404 0.7×	186 0.9×	325 2.1×	140 1.1×	92 0.8×	21	722
Wenjuan Tang China	13	261 0.4×	242 1.1×	108 0.7×	217 1.7×	85 0.8×	25	539
Mehmet Emre Gürsoy United States	13	825 1.3×	76 0.3×	151 1.0×	100 0.8×	137 1.2×	34	976

Countries citing papers authored by Sawsan AbdulRahman

Since Specialization

Citations

This map shows the geographic impact of Sawsan AbdulRahman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sawsan AbdulRahman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sawsan AbdulRahman more than expected).

Fields of papers citing papers by Sawsan AbdulRahman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sawsan AbdulRahman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sawsan AbdulRahman. The network helps show where Sawsan AbdulRahman may publish in the future.

Co-authorship network of co-authors of Sawsan AbdulRahman

This figure shows the co-authorship network connecting the top 25 collaborators of Sawsan AbdulRahman. A scholar is included among the top collaborators of Sawsan AbdulRahman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sawsan AbdulRahman. Sawsan AbdulRahman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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7 of 7 papers shown

1.

AbdulRahman, Sawsan, Ouns Bouachir, Safa Otoum, & Azzam Mourad. (2024). CRAS-FL: Clustered resource-aware scheme for federated learning in vehicular networks. Vehicular Communications. 47. 100769–100769. 3 indexed citations

2.

AbdulRahman, Sawsan, Safa Otoum, & Ouns Bouachir. (2024). Federated learning on the go: Building stable clusters and optimizing resources on the road. Vehicular Communications. 51. 100870–100870.

3.

AbdulRahman, Sawsan, Ouns Bouachir, Safa Otoum, & Azzam Mourad. (2023). Overcoming Resource Bottlenecks in Vehicular Federated Learning: A Cluster-Based and QoS-Aware Approach. 419–424. 1 indexed citations

4.

AbdulRahman, Sawsan, Safa Otoum, Ouns Bouachir, & Azzam Mourad. (2023). Management of Digital Twin-Driven IoT Using Federated Learning. IEEE Journal on Selected Areas in Communications. 41(11). 3636–3649. 21 indexed citations

5.

AbdulRahman, Sawsan, et al.. (2022). Adaptive Upgrade of Client Resources for Improving the Quality of Federated Learning Model. IEEE Internet of Things Journal. 10(5). 4677–4687. 14 indexed citations

6.

AbdulRahman, Sawsan, Hanine Tout, Azzam Mourad, & Chamseddine Talhi. (2020). FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning. IEEE Internet of Things Journal. 8(6). 4723–4735. 218 indexed citations

7.

AbdulRahman, Sawsan, Hanine Tout, Hakima Ould‐Slimane, et al.. (2020). A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond. IEEE Internet of Things Journal. 8(7). 5476–5497. 504 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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