Federated Learning Greedy Aggregation Optimization for Non-Independently Identically Distributed Data

In the domain of federated learning, traditional federated averaging algorithms encounter difficulties in maximizing accuracy in non-IID data circumstances due to the large data volume and the low model efficiency ratio of participants. To address the problem of non-IID data among participants in FL, this paper puts forward a novel greedy aggregation algorithm based on the F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ap</inf> model evaluation, named FedEGA. FedEGA incorporates models from each participant into the global model successively as potential components and builds the global model by evenly distributing multiple model weights that are fine-tuned with various hyperparameter configurations. During the construction process, a dynamic weight parameter mechanism is adopted to balance the accuracy and precision evaluation of the model. By using validation set scores on the central server, FedEGA ranks models in descending order, ensuring that the global model does not perform worse than the best individual model on the retained validation set, thereby alleviating issues caused by client drift and the hindrance to maximizing accuracy due to non-IID data. Experiments on public datasets and real-world data show that our algorithm surpasses classic FL algorithms such as Federated Averaging (FedAvg), Federated Proximal (FedProx) optimization, and Federated Self-Regularization (FedSR) in terms of maximum accuracy and communication efficiency in non-IID scenarios. Additionally, experiments with imbalanced data confirm its stronger robustness and generalization capabilities.