Federated learning and edge computing have resulted in the broad adoption of internet of Things (IoT) due to their fast reaction times and low connection c
This book provides a comprehensive and self-contained introduction to Federated Learning, ranging from the basic knowledge and theories to various key applications, and the privacy and incentive factors are the focus of the whole book. This book is timely needed since Federated Learning is getting ...
Bao X, Su C, Xiong Y, Huang W, Hu Y (2019) Flchain: a blockchain for auditable federated learning with trust and incentive. In: International conference on big data computing and communications (BIGCOM). IEEE, pp 151–159 Basu P, Roy TS, Naidu R, Muftuoglu Z (2021) Privacy enabled...
data privacydecision makinginternet of thingsThe incentive mechanism of federated learning has been a hot topic,but little research has been done on the compensation of privacy loss.To this end,this study uses the Local SGD federal learning framework and gives a theoretical analysis under...
四、Split Learning Advantages: 1) Enables the reduction in client-side computation in comparison to FL. 2) A certain level of privacy Weakness: 1)The training takes place in arelay-basedapproach, where the server trains with one client and then move to another client sequentially. (Slow.)...
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning Almost Cost-Free Communication in Federated Best Arm Identification Incentive-Boosted Federated Crowdsourcing Complement Sparsification: Low-Ov...
learningwireless-communicationdistributed-optimizationinterpretabilityneural-architecture-searchfederated-learningcontinual-learningvertical-federated-learningnon-iiddecentralized-federated-learninghierarchical-federated-learningadversarial-attack-and-defensecommunication-efficiencystraggler-problemcomputation-efficiencyincentive-...
various aspects likeprivacy preservation,data quality assessmentbefore aggregation, quality-aware incentive mechanisms, and reduction of high bandwidth usage by the end users[204]. Furthermore, federated learning can be incorporated withblockchainanddifferential privacyto enhance users’ privacy preservation[...
《2021-IEEE-FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation》提出了一种质量感知拍卖方法,将获胜者选择问题描述为一个 NP 难的学习质量最大化问题,并基于迈尔森定理设计了一个贪婪算法来执行实时任务分配和报酬分配。 2.1.3 声誉机制 《2020-Collaborative fairness in ...
《26,Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive》设计客户端共同参与深度学习模型训练的协同训练框架。 《27,FLchain: A blockchain for auditable federated learning with trust and incentive》提出FLChain来构建一个分散的、可公开审计的、健康的、有信任和激励的联邦...