Adapted Weighted Aggregation in Federated Learning

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.

Authors

• Yitong Tang University of British Columbia, 2329 West Mall Trusted and Efficient AI (TEA) Lab

Keywords

• Computer Vision
• Fairness
• FedAW
• Federated