Fedcir: Client-invariant representation learning for federated non-iid features

[PDF] arxiv.org

Understanding and improving model averaging in federated learning on heterogeneous data

T Zhou, Z Lin, J Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Model averaging is a widely adopted technique in federated learning (FL) that aggregates
multiple client models to obtain a global model. Remarkably, model averaging in FL yields a …
被引用次数：4 相关文章 所有 4 个版本
[PDF] arxiv.org

Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System

T Zhou, J Yu, J Zhang, DHK Tsang - arXiv preprint arXiv:2402.09729, 2024 - arxiv.org
This paper investigates resource allocation to provide heterogeneous users with customized
virtual reality (VR) services in a mobile edge computing (MEC) system. We first introduce a …
相关文章 所有 2 个版本

Mode Connectivity in Federated Learning with Data Heterogeneity

T Zhou, J Zhang, DHK Tsang - 2023 57th Asilomar Conference …, 2023 - ieeexplore.ieee.org
Federated learning (FL) allows multiple clients to train a global model while keeping data
locally. It has been well recognized that FL suffers from data heterogeneity, leading to drifts …
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