(论文链接:Decentralized Knowledge Acquisition for Mobile Internet Applications) Asychronous FL Training Rounds(联邦学习异步训练轮数):客户端数量越多,通信瓶颈和计算成本的风险就越高。很少有研究工作通过在联邦学习训练期间以最小的通信成本为目标来解决通信效率问题。相关研究提出使用神经网络的分层异步更新、基于模型...
An emerging model, called Federated Learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privacy preserving decentralized approach, which keeps raw data on devices and involves local ML training while eliminating...
《30,Mobile edge computing, blockchain and reputation-based crowdsourcing iot federated learning: A secure, decentralized and privacy-preserving system》一个基于区块链的信誉系统,一开始每个客户信誉值相同,贡献正确有用的模型参数信誉值增加,上传恶意参数信誉值减小,声誉高的客户下一轮训练更容易被选到 《31,T...
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Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications wher
本文提出的框架名字叫BFLC(Blockchain-based Federated Learning framework with Committee consensus ) BFLC 传统的中心化的联邦学习框架是由中心服务器向向客户端节点下发当前的全局模型,客户端节点收到后进行本地模型训练,完成后,向服务器上传模型更新,服务器聚合形成新的全局模型。迭代上述过程,直到全局模型精度或通...
Federated learning (FL) is a decentralized machine learning approach, where multiple entities, typically devices or edge servers, collaboratively train a shared model while keeping their training data locally. This enables these entities to train the model on their local datasets and then exchange just...
sharingofdata.Uptillnow,federatedlearninghasbeen widelyacceptedasawayofaggregatingdecentralizeddata andhastriggerednumerousapplications[Hardetal.,2018; McMahanetal.,2017;Yangetal.,2018]. Nevertheless,onecommonassumptionthatexistingworks onfederatedlearningmakeisthat,althoughasingleclient ...
小鱼关心个性化联邦学习(PFL, Personalized Federated Learning)[1]的发展,而在个性化联邦学习中,如果希望在各参与方(客户端)上学习个性化的模型,基于架构的方法有参数解耦和知识蒸馏两大类。正好小鱼调研到了一篇总结联邦学习中的知识蒸馏的使用指南[2]Knowledge Distillation for Federated Learning: a Practical Guide,...
A survey on federated learning for security and privacy in healthcare applications 2023, Computer Communications Citation Excerpt : The approach performs seizure detection by evaluating ECG signals. On the other hand, the Fed-ReMECS framework [63] uses FL to efficiently and scalably real-time emotio...