Abstract: The emergence of federated learning solves the problem of "data silos" in traditional machine learning.Federated learning enables the training of collective models while protecting the privacy of the client's local data.When the client’s dataset is independently identically distributed(IID) data,federated learning can achieve an accuracy similar to that of centralized machine learning.However,in real scenarios,due to differences in client devices and geographic locations,there are often cases where client’s dataset contain noisy data and non-independent identical distribution(Non-IID).Therefore,this paper proposes a CutMix-based federated learning framework,namely CutMix enhanced federated learning(CEFL),which first filters out noisy data through data cleaning algorithms and then trains through CutMix-based data enhancement.Compared with the traditional federated learning algorithm,the accuracy of CutMix enhanced federated learning can be improved by 23% and 19% for the model on Non-IID dataset.

Key words: Federated learning, Non-independent identically distributed data, Data cleaning, Data augmentation, Saliency detection