of labels (maybe only 2 labels), though the data on all clients contains 10 labels such as MNIST dataset. In thepractical non-IIDscenario, Dirichlet distribution is utilized (please refer to thispaperfor details). We can inputbalancefor the iid scenario, where the data are uniformly ...
The origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to
Pathological non-IID: In this case, each client only holds a subset of the labels, for example, just 2 out of 10 labels from the MNIST dataset, even though the overall dataset contains all 10 labels. This leads to a highly skewed distribution of data across clients. ...
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Practical non-IID: Here, we model the data distribution using a Dirichlet distribution, which results in a more realistic and less extreme imbalance. For more details on this, refer to thispaper. Additionally, we offer abalanceoption, where data amount is evenly distributed across all clients. ...
Personalized federated learning simulation platform with non-IID and unbalanced dataset - PFLlib/README.md at master · Simon007-heiyewuxing/PFLlib
of labels (maybe only 2 labels), though the data on all clients contains 10 labels such as MNIST dataset. In thepractical non-IIDscenario, Dirichlet distribution is utilized (please refer to thispaperfor details). We can inputbalancefor the iid scenario, where the data are uniformly ...