论文笔记——Federated learning framework for mobile edge computing networks
论文笔记——Federated learning framework for mobile edge computing networks 本论文着重研究的是联邦学习应用于需求预测类问题。算法 通常来讲，FL存在的一些问题：服务器 非独立同分布数据。客户训练数据集各不相同，给定的本地训练数据集不表明人口分布。 不平衡数据集。每一个客户的本地训练数据量不一样。这意味着不一样客户对训练价值观的可靠性不
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相关文章
- 1. 论文笔记——Federated learning framework for mobile edge computing networks
- 2. 论文笔记——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- 3. Federated Learning in Mobile Edge Networks: AComprehensive Survey(翻译)
- 4. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
- 5. 论文笔记Client-Edge-Cloud Hierarchical Federated Learning
- 6. 【论文阅读】Edge Computing Architecture for Mobile Crowdsensing
- 7. SecureBoost: A Lossless Federated Learning Framework论文笔记
- 8. Secure Federated Transfer Learning（论文笔记）
- 9. DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics
- 10. 论文笔记——Model Pruning Enables Efficient Federated Learning on Edge Devices