AAAI 2024technical2 citations
Adapted Weighted Aggregation in Federated Learning
Abstract
This study introduces FedAW, a novel federated learning algorithm that uses a weighted aggregation mechanism sensitive to the quality of client datasets, leading to better model performance and faster convergence on diverse datasets, validated using Colored MNIST.
BibTeX
@article{Tang_2024, title={Adapted Weighted Aggregation in Federated Learning}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30557}, DOI={10.1609/aaai.v38i21.30557}, abstractNote={This study introduces FedAW, a novel federated learning algorithm that uses a weighted aggregation mechanism sensitive to the quality of client datasets, leading to better model
performance and faster convergence on diverse datasets, validated using Colored MNIST.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Tang, Yitong}, year={2024}, month={Mar.}, pages={23763-23765} }