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UID:812@lincs.fr
DTSTART;TZID=Europe/Paris:20240117T140000
DTEND;TZID=Europe/Paris:20240117T150000
DTSTAMP:20240122T122125Z
URL:https://www.lincs.fr/events/data-poisoning-attacks-in-gossip-learning/
SUMMARY:Data Poisoning Attacks in Gossip Learning
DESCRIPTION:Traditional machine learning systems were designed in a
 centralized man-ner. In such designs\, the central entity maintains both
 the machine learningmodel and the data used to adjust the model’s
 parameters. As data central-ization yields privacy issues\, Federated
 Learning was introduced to reducedata sharing and have a central server
 coordinate the learning of multiple devices.\n\nWhile Federated Learning is
 more decentralized\, it still relies on a centralentity that may fail or be
 subject to attacks\, provoking the failure of thewhole system. Then\,
 Decentralized Federated Learning removes the need fora central server
 entirely\, letting participating processes handle the coordina-tion of the
 model construction. This distributed control urges studying thepossibility
 of malicious attacks by the participants themselves.\n\nWhile poisoning
 attacks on Federated Learning have been extensively stud-ied\, their
 effects in Decentralized Federated Learning did not get the samelevel of
 attention. Our work is the first to propose a methodology to
 assesspoisoning attacks in Decentralized Federated Learning in both churn
 free andchurn prone scenarios. Furthermore\, in order to evaluate our
 methodologyon a case study representative for gossip learning we extended
 the gossipysimulator with an attack injector module.\n\nAdditional
 information\nThis presentation is about a work in submission by Alexandre
 Pham\, Maria Potop-Butucaru\, Sebastien Tixeuil and Serge Fdida.
CATEGORIES:Seminars,Youtube
LOCATION:Room 4B01\, 19 place Marguerite Perey\, Palaiseau\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=19 place Marguerite Perey\,
 Palaiseau\, France;X-APPLE-RADIUS=100;X-TITLE=Room 4B01:geo:0,0
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TZID:Europe/Paris
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DTSTART:20231029T020000
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