Strategies for Reducing the Communication and Computation Costs in Cross-Silo Federated Learning: A Comprehensive Review
Federated learning is an innovative approach that allows collaboration across distributed clients while maintaining data privacy. Despite its numerous benefits, several issues persist in this domain. This comprehensive review examines the critical problems of communication and computation (C�...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11015825/ |
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| author | Vineetha Pais Santhosha Rao Balachandra Muniyal |
| author_facet | Vineetha Pais Santhosha Rao Balachandra Muniyal |
| author_sort | Vineetha Pais |
| collection | DOAJ |
| description | Federated learning is an innovative approach that allows collaboration across distributed clients while maintaining data privacy. Despite its numerous benefits, several issues persist in this domain. This comprehensive review examines the critical problems of communication and computation (C&C) costs in cross-silo federated learning environments, which significantly impact the scalability and practical adoption of the system. The research presents a novel multi-dimensional analysis methodology to evaluate cost reduction techniques that consider privacy, accuracy, scalability, and adaptability. The methodical investigation reveals that while existing approaches can substantially reduce communication overhead in controlled environments, ensuring model convergence and privacy guarantees remains challenging across diverse scenarios. Through detailed case studies spanning smart city deployments, healthcare, and finance sectors, the study demonstrates how various C&C optimization strategies perform differently in real-world applications. The review introduces a systematic taxonomy of cost-reduction techniques and proves that hybrid approaches combining multiple optimization methods can maintain model performance while optimizing resource utilization. The review concludes by presenting a unified roadmap for developing adaptive solutions that balance privacy, efficiency, and scalability requirements in cross-silo contexts and identifying crucial research gaps. This compilation of recent developments focuses on areas that require further investigation to enhance real-world deployments and provides practitioners with actionable guidelines for implementing effective federated learning systems. |
| format | Article |
| id | doaj-art-bdee4b6dd19c4aceafa6ceb8dbeadec0 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bdee4b6dd19c4aceafa6ceb8dbeadec02025-08-20T02:19:38ZengIEEEIEEE Access2169-35362025-01-0113933859341610.1109/ACCESS.2025.357393311015825Strategies for Reducing the Communication and Computation Costs in Cross-Silo Federated Learning: A Comprehensive ReviewVineetha Pais0https://orcid.org/0000-0003-3525-1523Santhosha Rao1https://orcid.org/0000-0001-9511-3048Balachandra Muniyal2https://orcid.org/0000-0002-4839-0082Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaFederated learning is an innovative approach that allows collaboration across distributed clients while maintaining data privacy. Despite its numerous benefits, several issues persist in this domain. This comprehensive review examines the critical problems of communication and computation (C&C) costs in cross-silo federated learning environments, which significantly impact the scalability and practical adoption of the system. The research presents a novel multi-dimensional analysis methodology to evaluate cost reduction techniques that consider privacy, accuracy, scalability, and adaptability. The methodical investigation reveals that while existing approaches can substantially reduce communication overhead in controlled environments, ensuring model convergence and privacy guarantees remains challenging across diverse scenarios. Through detailed case studies spanning smart city deployments, healthcare, and finance sectors, the study demonstrates how various C&C optimization strategies perform differently in real-world applications. The review introduces a systematic taxonomy of cost-reduction techniques and proves that hybrid approaches combining multiple optimization methods can maintain model performance while optimizing resource utilization. The review concludes by presenting a unified roadmap for developing adaptive solutions that balance privacy, efficiency, and scalability requirements in cross-silo contexts and identifying crucial research gaps. This compilation of recent developments focuses on areas that require further investigation to enhance real-world deployments and provides practitioners with actionable guidelines for implementing effective federated learning systems.https://ieeexplore.ieee.org/document/11015825/Communication costcomputation costcross-silofederated learningtaxonomy |
| spellingShingle | Vineetha Pais Santhosha Rao Balachandra Muniyal Strategies for Reducing the Communication and Computation Costs in Cross-Silo Federated Learning: A Comprehensive Review IEEE Access Communication cost computation cost cross-silo federated learning taxonomy |
| title | Strategies for Reducing the Communication and Computation Costs in Cross-Silo Federated Learning: A Comprehensive Review |
| title_full | Strategies for Reducing the Communication and Computation Costs in Cross-Silo Federated Learning: A Comprehensive Review |
| title_fullStr | Strategies for Reducing the Communication and Computation Costs in Cross-Silo Federated Learning: A Comprehensive Review |
| title_full_unstemmed | Strategies for Reducing the Communication and Computation Costs in Cross-Silo Federated Learning: A Comprehensive Review |
| title_short | Strategies for Reducing the Communication and Computation Costs in Cross-Silo Federated Learning: A Comprehensive Review |
| title_sort | strategies for reducing the communication and computation costs in cross silo federated learning a comprehensive review |
| topic | Communication cost computation cost cross-silo federated learning taxonomy |
| url | https://ieeexplore.ieee.org/document/11015825/ |
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