The DOI matches its Crossref record; 154 of the 154 references it cites were found in a registry or an index.
Model aggregation techniques in federated learning: A comprehensive survey
Open accessAbstract
Federated learning (FL) is a distributed machine learning (ML) approach that enables models to be trained on client devices while ensuring the privacy of user data. Model aggregation, also known as model fusion, plays a vital role in FL. It involves combining locally generated models from client devices into a single global model while maintaining user data privacy. However, the accuracy and reliability of the resulting global model depend on the aggregation method chosen, making the selection of an appropriate method crucial. Initially, the simple averaging of model weights was the most commonly used method. However, due to its limitations in handling low-quality or malicious models, alternative techniques have been explored. As FL gains popularity in various domains, it is crucial to have a comprehensive understanding of the available model aggregation techniques and their respective strengths and limitations. However, there is currently a significant gap in the literature when it comes to systematic and comprehensive reviews of these techniques. To address this gap, this paper presents a systematic literature review encompassing 201 studies on model aggregation in FL. The focus is on summarizing the proposed techniques and the ones currently applied for model fusion. This survey serves as a valuable resource for researchers to enhance and develop new aggregation techniques, as well as for practitioners to select the most appropriate method for their FL applications.
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Cites
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Cited by
Survey: federated learning data security and privacy-preserving in edge-Internet of Things
Li, H., Ge, L., & Tian, L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10774-7 · Cited by 112
“…analysis of the more advanced FL aggregation algorithms, and discusses and summarizes the model aggregation algorithms under different FL and EC application scenarios by comparing different advanced schemes in terms of data security, model performance, and system efficiency (Qi et al. 2023).”
A Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy
Manzoor, H. U., Shabbir, A., Chen, A., Flynn, D., & Zoha, A. (2024) · Future Internet · doi:10.3390/fi16100374 · Cited by 58
Adam, M., & Baroudi, U. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3506214 · Cited by 46
Blockchain-Inspired Collaborative Cyber-Attacks Detection for Securing Metaverse
Zainudin, A., Putra, M. A. P., Alief, R. N., Akter, R., Kim, D.-S., & Lee, J.-M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3364247 · Cited by 35
Lv, F., Wang, H., Pan, Z., Sun, R., Si, S., Zhang, W., Lv, S., & Sun, L. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111459 · Cited by 12
Metadata
- Authors
- Qi, P., Chiaro, D., Guzzo, A., Ianni, M., Fortino, G., & Piccialli, F.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 150
- Pages
- 272-293
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W4386608426
- Cited by
- 452
- References
- 154
- FWCI
- 39.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Qi, P., Chiaro, D., Guzzo, A., Ianni, M., Fortino, G., & Piccialli, F. (2024). Model aggregation techniques in federated learning: A comprehensive survey. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2023.09.008
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