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@Misc{PersonalizedFL, howpublished = {\url{https://github.com/microsoft/PersonalizedFL}}, title = {PersonalizedFL: Personalized Federated Learning Toolkit}, author = {Lu, Wang and Wang, Jindong} } Contact Wang lu:luwang@ict.ac.cn
代码:GitHub - CharlieDinh/pFedMe: Personalized Federated Learning with Moreau Envelopes (pFedMe) using Pytorch (NeurIPS 2020) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) (人工智能领域顶会) Backgrounds A main challenge: Statistical diversity affects FL’ s performance and con...
[1] Lu, Wang and Wang, Jindong. PersonalizedFL: Personalized Federated Learning Toolkit. GitHub - microsoft/PersonalizedFL: Personalized federated learning codebase for research [2] McMahan, Brendan, et al. "Communication-efficient learning of deep networks from decentralized data." Artificial intelli...
Federated learning (FL) allows clients to train a deep learning model collaboratively while maintaining their private data locally. One challenging problem facing FL is that the model utility drops significantly once the data distribution gets heterogeneous, or non-i.i.d, among clients. A promising...
GitHub 1 .Keywords: federated learning, personalization, privacy-preserving, algorithm library,collaborative learning1 IntroductionFederated Learning (FL) has gained signif i cant attention due to its ability to perform dis-tributed machine learning while ensuring privacy preservation (Yang et al., 2019...
,wenkehuang,yemang}@whu.edu.cn https://github.com/xiyuanyang45/FedAS Abstract Personalized Federated Learning (PFL) is primarily de- signed to provide customized models for each client to bet- ter fit the non-iid distributed client data, which is a inherent challenge ...
In Federated learning (FL), multiple clients collaborate to learn a model through a central server but keep the data decentralized. Personalized federated learning (PFL) further extends FL to handle data heterogeneity between clients by learning personalized models. In both FL and PFL, all clients ...
This repository not only implements pFedMe but also FedAvg, and Per-FedAvg algorithms. (Federated Learning using Pytorch) Software requirements: numpy, scipy, torch, Pillow, matplotlib. To download the dependencies:pip3 install -r requirements.txt ...
Chandra, “Federated Learning with Non-IID Data,” arXiv:1806.00582, 2018. Jeong等人提出了FAug,这是一种联邦增强方法,在FL服务器上训练生成对抗网络(GAN)模型。将少数群体的一些数据样本上传到服务器上以训练GAN模型。然后,将训练好的GAN模型分发给每个客户端,生成额外的数据,以增加其本地数据,从而生成一个I...