the methods evaluated in the paper FedDF: Ensemble Distillation for Robust Model Fusion in Federated Learning. For the detailed instructions and more examples, please refer to the file codes/FedDF-code/README.md. Reference If you use the code in this repository, please consider to cite the fo...
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代码放在了github上面。 3. 训练。所有的实验都是用的100轮的训练(也就是device和server更新参数的轮数),选择了100个device作为participants。 下面是训练的实验结果 实验里面扩展的2个点是 是也实验了引入差分隐私的效果如何。结论是每个参与方级别的差分隐私能够对抗backdoor攻击,但是会导致主任务的精度下降。 2. ...
当然,对于上述的这种现象产生的原因也是一个很好的研究点。 code inference: GitHub - Intelligent-Computing-Lab-Yale/FedSNN: Source code for the paper "Federated Learning with Spiking Neural Networks".github.com/Intelligent-Computing-Lab-Yale/FedSNN...
Communication-efficient learning of deep networks from decentralized data Artificial Intelligence and Statistics, PMLR (2017), pp. 1273-1282 View in ScopusGoogle Scholar [2] Zhuang W., Gan X., Wen Y., Zhang S. Easyfl: A low-code federated learning platform for dummies IEEE Internet Things J...
Federated learning, a decentralized paradigm, offers the potential to train models across multiple devices while preserving data privacy. However, challenges such as malicious actors and model parameter leakage have raised concerns. To tackle these issues, we introduce a game-theoretic, trustworthy anti...
The NVFLARE framework (https://nvidia.github.io/NVFlare/, accessed on 15 June 2023) has been selected for the development of the FL platform as it supports both deep learning and traditional ML algorithms, as well as horizontal and vertical FL. It includes built-in FL aggregation algorithms...
OpenFL is available on GitHub, along with tutorials and documentation to help organizations get started with their own FL projects. OpenFL is designed to be compatible with any ML or deep learning (DL) framework, and has tutorials and multiple examples using TensorFlow, PyTorch and MXNet. OpenFL...
联邦学习(Federated Learning)是一种新兴的人工智能基础技术,在 2016 年由谷歌最先提出,原本用于解决安卓手机终端用户在本地更新模型的问题,其设计目标是在保障大数据交换时的信息安全、保护终端数据和个人数据隐私、保证合法合规的前提下,在多参与方或多计算结点之间开展高效率的机器学习。其中,联邦学习可使用的机器学习...
code: https://github.com/felisat/clustered-federated-learning 编辑:古月 目的:传统联邦学习中,存在这个假设: 训练一组模型,使得所有得到用户都能满足最小化风险函数的目标: 假设1 但是,显然这种假设并不是对于所有用户都满足,因为两个用户之间的数据分布很容易是很不相似的,对应于FL中的Non_IID问题。