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Federated learning (FL) produces an effective global model by aggregating multiple client weights trained on their private data. However, it is common that the data are not independently and identically distributed (non-IID) across different clients, which greatly degrades the performance of the global model. We observe that existing FL approaches mostly ignore the distribution information of client-side private data. Actually, the distribution information is a kind of structured knowledge about the data itself, and it also represents the mutual clustering relations of data examples. In this work, we propose a novel approach, namely Federated Distribution Knowledge Transfer (FedDKT), that alleviates heterogeneous FL by extracting and transferring the distribution knowledge from diverse data. Specifically, the server learns a lightweight generator to generate data and broadcasts it to the sampled clients, FedDKT decouples the feature representations of the generated data and transfers t ....
Federated learning (FL) strives to enable collaborative training of deep models on the distributed clients of different data without centrally aggregating raw data and hence improving data privacy. Nevertheless, a central challenge in training classification models in the federated system is learning with non-IID data. Most of the existing work is dedicated to eliminating the heterogeneous influence of non-IID data in a federated system. However, in many real-world FL applications, the co-occurrence of data heterogeneity and long-tailed distribution is unavoidable. The universal class distribution is long-tailed, causing them to become easily biased towards head classes, which severely harms the global model performance. In this work, we also discovered an intriguing fact that the classifier logit vector (i.e., pre-softmax output) introduces a heterogeneity drift during the learning process of local training and global optimization, which harms the convergence as well as model performa ....
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