Pisces
Zhifeng Jiang, Wei Wang, Baochun Li, Bo Li
Abstract
Federated learning (FL) is typically performed in a synchronous parallel manner, and the involvement of a slow client delays the training progress. Current FL systems employ a participant selection strategy to select fast clients with quality data in each iteration. However, this is not always possible in practice, and the selection strategy has to navigate a knotty tradeoff between the speed and the data quality.
Topics & Concepts
Computer scienceSelection (genetic algorithm)Quality (philosophy)Artificial intelligenceTraining setMachine learningPhilosophyEpistemologyPrivacy-Preserving Technologies in DataMobile Crowdsensing and CrowdsourcingCryptography and Data Security