Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning
This addresses challenges in federated learning for applications requiring data privacy and distributed training, but it appears incremental as it builds on existing FL paradigms with a new testing scheme.
The paper tackles the problems of evaluating model quality, handling unbalanced models, and mitigating adversarial attacks in federated learning by introducing FedTest, a framework where users test each other's models to assign scores for aggregation and malicious detection. Numerical results show it accelerates convergence rates and reduces the impact of malicious users, enhancing system efficiency and robustness.
Federated Learning (FL) has emerged as a significant paradigm for training machine learning models. This is due to its data-privacy-preserving property and its efficient exploitation of distributed computational resources. This is achieved by conducting the training process in parallel at distributed users. However, traditional FL strategies grapple with difficulties in evaluating the quality of received models, handling unbalanced models, and reducing the impact of detrimental models. To resolve these problems, we introduce a novel federated learning framework, which we call federated testing for federated learning (FedTest). In the FedTest method, the local data of a specific user is used to train the model of that user and test the models of the other users. This approach enables users to test each other's models and determine an accurate score for each. This score can then be used to aggregate the models efficiently and identify any malicious ones. Our numerical results reveal that the proposed method not only accelerates convergence rates but also diminishes the potential influence of malicious users. This significantly enhances the overall efficiency and robustness of FL systems.