F1 Score Based Weighted Asynchronous Federated Learning
Sneha Sree Yarlagadda, Sai Harshith Tule, Karthik Myada · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: The domain of federated learning has observed remarkable developments in recent years, enabling collaborative model training while preserving data privacy. This paper discusses several recent advancements in the field of federated learning, particularly in asynchronous and weighted federated learning. A novel approach within the federated learning paradigm titled "F1 Score Based Weighted Asynchronous Federated Learning" is introduced. The approach addresses issues of biased aggregation and device heterogeneity by assigning weights to devices based on their F1 scores, prioritizing those with superior performance in classification. Leveraging the asynchronous nature of federated learning, this approach enhances both resource efficiency and convergence speed. By incorporating F1 scores into the weighting mechanism, a balanced emphasis on precision and recall is achieved. It can optimize resource utilization, expediting the learning process by focusing on updates from devices with more accurate and valuable information. This approach can enhance collaborative model improvement while preserving data privacy in federated learning.