APA (7th ed.) Citation

Islam, F., Mahmood, A., Wang, Y., Perera, M., & Tahermazandarani, M. (2026). Federated Learning With Noisy and Imbalanced Data: A Contrastive Reliability-Based Framework for Long-Tail-Aware Client Selection. IEEE Open Journal of the Communications Society, Communications Society, IEEE Open Journal of the, IEEE Open J. Commun. Soc., 7, 11610. https://doi.org/10.1109/OJCOMS.2026.3733565

Chicago Style (17th ed.) Citation

Islam, F., A. Mahmood, Y. Wang, M.M Perera, and M. Tahermazandarani. "Federated Learning With Noisy and Imbalanced Data: A Contrastive Reliability-Based Framework for Long-Tail-Aware Client Selection." IEEE Open Journal of the Communications Society, Communications Society, IEEE Open Journal of the, IEEE Open J. Commun. Soc. 7 (2026): 11610. https://doi.org/10.1109/OJCOMS.2026.3733565.

MLA (9th ed.) Citation

Islam, F., et al. "Federated Learning With Noisy and Imbalanced Data: A Contrastive Reliability-Based Framework for Long-Tail-Aware Client Selection." IEEE Open Journal of the Communications Society, Communications Society, IEEE Open Journal of the, IEEE Open J. Commun. Soc., vol. 7, 2026, p. 11610, https://doi.org/10.1109/OJCOMS.2026.3733565.