Client Specific Dynamic Aggregation for Non-IID Federated Learning

Analyzing big data using federated learning (FL) requires distributing the data to different clients to process locally and sending the model parameters to the global server for aggregation. In a real scenario, big data distribution is nonindependent and identically distributed (non-IID). Aggregating client updates is an important task in federated learning. Federated averaging (FedAvg) is the simplest and most popularly used method in FL. However, it is unable to handle heterogeneous (non-IID) data. To address this, in this paper we propose a Client Specific Dynamic Aggregation (CSDA) strategy focusing on dynamic aggregation based on client-specific metrics, aiming to improve the robustness and performance of the global model. Each client's contribution is weighted by the quality and performance of their local updates, enhancing the overall federated learning process. Extensive experimentation has demonstrated that the proposed CSDA strategy, in conjunction with advanced comparison techniques, has the potential to greatly enhance the accuracy of each client across three real-world datasets.