We argue that federated learning is generically vulnerable to backdoors and other model-poisoning attacks. First, when training with millions of participants, it is impossible to ensure that none of them are malicious. The possibility of training with multiple malicious participants is explicitly acknowl...
Deborah Estrin课题组针对联邦学习后门攻击提出了新的攻击方法—模型中毒攻击方法。相关成果“How To Backdoor Federated Learning”发表在AISTATS 2020上。 在这篇文章之前,联邦学习中毒攻击偏向于在参与者本地的训练数据集中插入带有后门的数据,并通过参与者训练本地模型以及服务器聚合参与者模型最终使得训练完成后的模型...
Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other. For example, multiple smartphones can jointly train a next-word predictor for keyboards without revealing what individual users type. We demonstrate that an...
论文标题:How to Backdoor Federated Learning 作者:Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov 摘要 本文表明联邦学习易受某种 model-poisoning 攻击,这个攻击比只在训练集上的 poisoning 攻击更厉害。单个或多个恶意的参与者可以使用本文提出的 model replacement 在联合模型上注...
Federated LearningSecure AggregationMutual InformationFormal Privacy GuaranteeFederated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device. However, while data never leaves users' devices, ...
Federated Learning (FL) is a transformative, distributive computational approach that revolutionizes decision-making capabilities through decentralized data computation. Despite notable operational advantages stemming from FL implementation, the optimal selection of methods from the existing literatu...
11/20/2020: We are developing a new framework for backdoors with FL:Backdoors101.It extends to many new attacks (clean-label, physical backdoors, etc) and has improved user experience. Check it out! backdoor_federated_learning This code includes experiments for paper "How to Backdoor Federate...
To reiterate, the basic structure for a federated learning model shows that: Devices train locally Updates are sent to the central server The server aggregates updates into a global model Much like a traditional AI learning model, this process is repeated multiple times until the model reaches a...
Federated learning (FL) is an ML technique where data scientists collaboratively train a model orchestrated by a central server. This means that the training data is not centralized. The basic premise behind FL is that the AI model moves to meet the data, instead of the data moving to...
Solved using advanced deep learning techniques. Scalability Handled through cloud computing and distributed systems. Privacy Concerns Ensure compliance with regulations and implement federated learning for data security. Benefits of AI Recommendation Systems in 2025 ...