Abstract
—In the rapidly evolving landscape of federated learning (FL), hierarchical architectures are pivotal for improving computational efficiency and safeguarding data privacy. A key challenge in this research area is the optimal selection of edge servers, crucial for executing distributed learning tasks across multiple clients and servers efficiently. Traditional selection methods falter due to their inability to dynamically handle the uncertainties in network conditions and server capabilities. To addressing this weakness, we propose a fuzzy logic based approach that optimizes edge server selection in a novel smart way, thus enhancing resource allocation by efficiently handling the unpredictable nature of network environments and servers performance. This method is integrated with a previously developed scheme for selecting an optimal subset of clients,thereby establishing a comprehensive framework that significantly boosts the performance and reliability of FL networks.The performance of our approach is validated through real world experiments and the results demonstrate its superiority over existing methods in terms of accuracy and processing time
| Original language | English |
|---|---|
| Title of host publication | GLOBECOM 2024 - 2024 IEEE Global Communications Conference |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4364-4369 |
| Number of pages | 6 |
| ISBN (Electronic) | 979-8-3503-5125-5 |
| ISBN (Print) | 979-8-3503-5126-2 |
| DOIs | |
| Publication status | E-pub ahead of print - 11 Mar 2025 |
| Event | IEEE GLOBECOM 2024 - Cape Town, South Africa Duration: 8 Dec 2024 → 12 Dec 2024 |
Publication series
| Name | Proceedings - IEEE Global Communications Conference, GLOBECOM |
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| ISSN (Print) | 2334-0983 |
| ISSN (Electronic) | 2576-6813 |
Conference
| Conference | IEEE GLOBECOM 2024 |
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| Country/Territory | South Africa |
| City | Cape Town |
| Period | 8/12/24 → 12/12/24 |
Bibliographical note
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Funding
This research is supported in part by JSPS KAKENHI grant number 22K17880, JST ASPIRE Grant Number JPMJAP2325, in part by JSPS Bilateral Joint Research Project No. JPJSB120231002, and in part by ROIS NII Open Collaborative Research under Grant 24S0601.
| Funders | Funder number |
|---|---|
| Japan Society for Promotion of Scientific Research | 22K17880 |
| Japan Society for Promotion of Scientific Research | |
| Japan Science and Technology Agency | 24S0601, JPMJAP2325, JPJSB120231002 |
| Japan Science and Technology Agency |
Keywords
- Client Selection
- Edge Computing
- Federated Learning
- Fuzzy Logic
- IoT
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Hardware and Architecture
- Signal Processing