FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3102800317> ?p ?o ?g. }

• W3102800317 abstract "Time Series data has become present everywhere, thanks to affordable edge devices and sensors. Much of this data is valuable for decision making. To use this data for the forecasting task, the conventional centralized approach has shown deficiencies regarding extensive data communication and data privacy issues. Federated learning allows learning from decentralized data without the need to store it centrally. The data remains where it was generated, which guarantees privacy and reduces communication costs [1]. FL already naturally selects only a few nodes randomly at each round. They have non-iid data and also varying in amount. After some iteration of training, the central aggregator will generate a global model. That is, heavily learning depends on a coordinator, which causes scalability issues with large numbers of nodes, and besides, there is a single point of failure, which is not suitable for some applications. An example of this is predicting the online trajectory of moving nodes to manage traffic, or proactive resource allocation in vehicular networks. In this work, to tackle these problems, we use personalized distributed Federated Learning which is online, peer-to-peer and provides asynchronous communications. Each node in this network is a client for other existing nodes and use its local dataset to improve their models. At the same time, it is like a coordinating server that merges received models and personalized the model for itself. There can be as many models as many as the number of clients. We present three practical algorithms called DFed Avg, DFed Pow and DFed Best for the serverless federated learning of deep networks based on iterative model averaging, and an empirical evaluation which considers time series datasets and an LSTM model. DFed Avg merges models based on the technique used in Federated Averaging, while DFed Best, and DFed Pow at every iteration rounds use different methods to merge models. Our goal is to evaluate our optimization algorithms, not to achieve the best possible accuracy on these tasks. The experiments demonstrate that these approaches are rather robust and can have numerous clients with the dynamic, unbalanced and non-IID data distributions." @default.
• W3102800317 created "2020-11-23" @default.
• W3102800317 creator A5054224789 @default.
• W3102800317 date "2020-07-28" @default.
• W3102800317 modified "2023-10-18" @default.
• W3102800317 title "Federated learning for vehicle trajectory prediction" @default.
• W3102800317 hasPublicationYear "2020" @default.
• W3102800317 type Work @default.
• W3102800317 sameAs 3102800317 @default.
• W3102800317 citedByCount "0" @default.
• W3102800317 crossrefType "journal-article" @default.
• W3102800317 hasAuthorship W3102800317A5054224789 @default.
• W3102800317 hasConcept C111919701 @default.
• W3102800317 hasConcept C119857082 @default.
• W3102800317 hasConcept C120314980 @default.
• W3102800317 hasConcept C127413603 @default.
• W3102800317 hasConcept C151319957 @default.
• W3102800317 hasConcept C154945302 @default.
• W3102800317 hasConcept C162324750 @default.
• W3102800317 hasConcept C165136773 @default.
• W3102800317 hasConcept C180505990 @default.
• W3102800317 hasConcept C187736073 @default.
• W3102800317 hasConcept C2780451532 @default.
• W3102800317 hasConcept C29202148 @default.
• W3102800317 hasConcept C31258907 @default.
• W3102800317 hasConcept C41008148 @default.
• W3102800317 hasConcept C48044578 @default.
• W3102800317 hasConcept C62611344 @default.
• W3102800317 hasConcept C66938386 @default.
• W3102800317 hasConcept C77088390 @default.
• W3102800317 hasConceptScore W3102800317C111919701 @default.
• W3102800317 hasConceptScore W3102800317C119857082 @default.
• W3102800317 hasConceptScore W3102800317C120314980 @default.
• W3102800317 hasConceptScore W3102800317C127413603 @default.
• W3102800317 hasConceptScore W3102800317C151319957 @default.
• W3102800317 hasConceptScore W3102800317C154945302 @default.
• W3102800317 hasConceptScore W3102800317C162324750 @default.
• W3102800317 hasConceptScore W3102800317C165136773 @default.
• W3102800317 hasConceptScore W3102800317C180505990 @default.
• W3102800317 hasConceptScore W3102800317C187736073 @default.
• W3102800317 hasConceptScore W3102800317C2780451532 @default.
• W3102800317 hasConceptScore W3102800317C29202148 @default.
• W3102800317 hasConceptScore W3102800317C31258907 @default.
• W3102800317 hasConceptScore W3102800317C41008148 @default.
• W3102800317 hasConceptScore W3102800317C48044578 @default.
• W3102800317 hasConceptScore W3102800317C62611344 @default.
• W3102800317 hasConceptScore W3102800317C66938386 @default.
• W3102800317 hasConceptScore W3102800317C77088390 @default.
• W3102800317 hasLocation W31028003171 @default.
• W3102800317 hasOpenAccess W3102800317 @default.
• W3102800317 hasPrimaryLocation W31028003171 @default.
• W3102800317 hasRelatedWork W2116612304 @default.
• W3102800317 hasRelatedWork W2283463896 @default.
• W3102800317 hasRelatedWork W2530417694 @default.
• W3102800317 hasRelatedWork W2950745363 @default.
• W3102800317 hasRelatedWork W2977814456 @default.
• W3102800317 hasRelatedWork W2993233138 @default.
• W3102800317 hasRelatedWork W2994684563 @default.
• W3102800317 hasRelatedWork W3009947594 @default.
• W3102800317 hasRelatedWork W3013686349 @default.
• W3102800317 hasRelatedWork W3023042748 @default.
• W3102800317 hasRelatedWork W3045423569 @default.
• W3102800317 hasRelatedWork W3047304572 @default.
• W3102800317 hasRelatedWork W3089562172 @default.
• W3102800317 hasRelatedWork W3091296419 @default.
• W3102800317 hasRelatedWork W3100760018 @default.
• W3102800317 hasRelatedWork W3111009493 @default.
• W3102800317 hasRelatedWork W3111358184 @default.
• W3102800317 hasRelatedWork W3170957236 @default.
• W3102800317 hasRelatedWork W3187068037 @default.
• W3102800317 hasRelatedWork W3200470696 @default.
• W3102800317 isParatext "false" @default.
• W3102800317 isRetracted "false" @default.
• W3102800317 magId "3102800317" @default.
• W3102800317 workType "article" @default.