Enhancing dynamic resource management in decentralized federated learning for collaborative edge Internet of Things
Abstract Dynamic situations and applications are supported by the diverse devices and communication technologies that constitute the Internet of Things concept. Despite this, communication backlogs are common due to rising network demand and insufficient resource allocation. This study provides a wa...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-05-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13638-025-02459-8 |
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| _version_ | 1849728350884462592 |
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| author | Asma Aldrees Ashit Kumar Dutta Ahmed Emara Sana Shahab Zaffar Ahmed Shaikh Yousef Ibrahim Daradkeh Mohd Anjum |
| author_facet | Asma Aldrees Ashit Kumar Dutta Ahmed Emara Sana Shahab Zaffar Ahmed Shaikh Yousef Ibrahim Daradkeh Mohd Anjum |
| author_sort | Asma Aldrees |
| collection | DOAJ |
| description | Abstract Dynamic situations and applications are supported by the diverse devices and communication technologies that constitute the Internet of Things concept. Despite this, communication backlogs are common due to rising network demand and insufficient resource allocation. This study provides a way to fix resource allocation problems using Mutable Resource Allocation and Distributed Federated Learning. Inadequacies and backlogs in resources are identified at the edge of the network. As part of this procedure, edge devices are assigned to link resources and users after independently determining which resources cannot be allocated and which shortcomings are linked with them. Adapting to demand and learning suggestions, this allocation is flexible. By classifying resources as sufficient or inadequate, the learning suggestions help avoid backlogs. This enables edge devices to choose between allocation and response, which improves network flexibility by prioritizing inadequate resource allocation. Accordingly, the recommendation factor periodically affects modifications to the edge connection and its interaction with the Internet of Things platform. The suggestion is particularly strong for situations with changing backlogs to ensure that subsequent resource allocations align with preference-based learning. Claiming to improve connection, services, and resource allocation while decreasing backlogs and allocation times, this approach is a hot commodity. |
| format | Article |
| id | doaj-art-f43ad58cdfbd4ca0aa5a42e25861eea3 |
| institution | DOAJ |
| issn | 1687-1499 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj-art-f43ad58cdfbd4ca0aa5a42e25861eea32025-08-20T03:09:34ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992025-05-012025112310.1186/s13638-025-02459-8Enhancing dynamic resource management in decentralized federated learning for collaborative edge Internet of ThingsAsma Aldrees0Ashit Kumar Dutta1Ahmed Emara2Sana Shahab3Zaffar Ahmed Shaikh4Yousef Ibrahim Daradkeh5Mohd Anjum6Department of Informatics and Computer Systems, College of Computer Science, King Khalid UniversityDepartment of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa UniversityDepartment of Electrical Engineering, University of Business and TechnologyDepartment of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman UniversityDepartment of Computer Science and Information Technology, Benazir Bhutto Shaheed University LyariDepartment of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz UniversityDepartment of Computer Engineering, Aligarh Muslim UniversityAbstract Dynamic situations and applications are supported by the diverse devices and communication technologies that constitute the Internet of Things concept. Despite this, communication backlogs are common due to rising network demand and insufficient resource allocation. This study provides a way to fix resource allocation problems using Mutable Resource Allocation and Distributed Federated Learning. Inadequacies and backlogs in resources are identified at the edge of the network. As part of this procedure, edge devices are assigned to link resources and users after independently determining which resources cannot be allocated and which shortcomings are linked with them. Adapting to demand and learning suggestions, this allocation is flexible. By classifying resources as sufficient or inadequate, the learning suggestions help avoid backlogs. This enables edge devices to choose between allocation and response, which improves network flexibility by prioritizing inadequate resource allocation. Accordingly, the recommendation factor periodically affects modifications to the edge connection and its interaction with the Internet of Things platform. The suggestion is particularly strong for situations with changing backlogs to ensure that subsequent resource allocations align with preference-based learning. Claiming to improve connection, services, and resource allocation while decreasing backlogs and allocation times, this approach is a hot commodity.https://doi.org/10.1186/s13638-025-02459-8Edge computingFederated learningInternet of ThingsResource allocationCommunication backlogsRecommendation factor |
| spellingShingle | Asma Aldrees Ashit Kumar Dutta Ahmed Emara Sana Shahab Zaffar Ahmed Shaikh Yousef Ibrahim Daradkeh Mohd Anjum Enhancing dynamic resource management in decentralized federated learning for collaborative edge Internet of Things EURASIP Journal on Wireless Communications and Networking Edge computing Federated learning Internet of Things Resource allocation Communication backlogs Recommendation factor |
| title | Enhancing dynamic resource management in decentralized federated learning for collaborative edge Internet of Things |
| title_full | Enhancing dynamic resource management in decentralized federated learning for collaborative edge Internet of Things |
| title_fullStr | Enhancing dynamic resource management in decentralized federated learning for collaborative edge Internet of Things |
| title_full_unstemmed | Enhancing dynamic resource management in decentralized federated learning for collaborative edge Internet of Things |
| title_short | Enhancing dynamic resource management in decentralized federated learning for collaborative edge Internet of Things |
| title_sort | enhancing dynamic resource management in decentralized federated learning for collaborative edge internet of things |
| topic | Edge computing Federated learning Internet of Things Resource allocation Communication backlogs Recommendation factor |
| url | https://doi.org/10.1186/s13638-025-02459-8 |
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