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FL-Torrent: decentralised AI for the masses
dc.contributor.author | García-Recuero, Álvaro | |
dc.contributor.author | Laoutaris, Nikolaos | |
dc.date.accessioned | 2022-10-31T09:29:28Z | |
dc.date.available | 2022-10-31T09:29:28Z | |
dc.date.issued | 2022-06-08 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12761/1639 | |
dc.description.abstract | Google has made Federated Learning (FL) readily available to the public with their software and APIs as TensorFlow, but vendor lock-in is still a problem for experimental deployments of Federated Learning. It is therefore necessary to abide by the design of such proprietary APIs, implying full trust in their safety and security against backdoor or label flipping attacks, similarly to what used to occur in proprietary Operating Systems a while ago. More recently, several criticisms about the privacy of novel cookie killer applications of FL such as FL-of-Cohorts (FLoC) has also driven to the discontinuity of this web based system by Google, which just shows how industry solutions are often imperfect naturally. FL-Torrent aims to decentralise and thus democratise this vendor dominated API ecosystem for Federated Learning. | es |
dc.language.iso | eng | es |
dc.title | FL-Torrent: decentralised AI for the masses | es |
dc.type | conference object | es |
dc.conference.place | Madrid, Spain | es |
dc.conference.title | 12th IMDEA Networks Annual International Workshop | * |
dc.event.type | workshop | es |
dc.pres.type | poster | es |
dc.type.hasVersion | AM | es |
dc.rights.accessRights | open access | es |
dc.subject.keyword | federated learning | es |
dc.subject.keyword | privacy | es |
dc.subject.keyword | security | es |
dc.subject.keyword | systems | es |
dc.description.refereed | FALSE | es |
dc.description.status | pub | es |