Tackling data-heterogeneity variations in federated learning via adaptive aggregate weights

Author:

Yin QiaoyunORCID,Feng ZhiyongORCID,Li Xiaohong,Chen ShizhanORCID,Wu HongyueORCID,Han GaoyongORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference55 articles.

1. Communication-efficient learning of deep networks from decentralized data;McMahan,2017

2. Towards personalized federated learning;Tan;IEEE Trans. Neural Netw. Learn. Syst.,2022

3. A survey on federated learning;Zhang;Knowl.-Based Syst.,2021

4. Optimized compressed sensing for communication efficient federated learning;Wu;Knowl.-Based Syst.,2023

5. A blockchain-based framework for federated learning with privacy preservation in power load forecasting;Mao;Knowl.-Based Syst.,2024

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