Federated Learning Tutorial

Noise-Robust Federated Learning With Model Heterogeneous Clients

Abstract: Federated Learning (FL) enables multiple devices to collaboratively train models without sharing their raw data. Considering that clients may prefer to design their own models independently, ...

IEEE

Federated Learning Based on Model Discrepancy and Variance Reduction

Abstract: In federated learning (FL), the heterogeneity of data and asynchronous participation of clients have been observed to induce the local client’s model discrepancy with high variance, leading ...

EurekAlert!

A federated anti-forgetting representation method based on hybrid model architecture and gradient truncation

Anti-forgetting representation learning method reduces the weight aggregation interference on model memory and augments the ...

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Noise-Robust Federated Learning With Model Heterogeneous Clients

Federated Learning Based on Model Discrepancy and Variance Reduction

A federated anti-forgetting representation method based on hybrid model architecture and gradient truncation

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