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UID:790@lincs.fr
DTSTART;TZID=Europe/Paris:20231025T103000
DTEND;TZID=Europe/Paris:20231025T113000
DTSTAMP:20231102T134228Z
URL:https://www.lincs.fr/events/opportunistic-federated-learning-an-explor
 ation-of-egocentric-collaboration-for-pervasive-computing-applications/
SUMMARY:Opportunistic Federated Learning: An Exploration of Egocentric
 Collaboration for Pervasive Computing Applications.
DESCRIPTION:Federated learning (FL) is an emerging distributed learning
 technique\, through  which models can be trained using the data collected
 by user devices in resource-constrained situations while protecting user
 privacy. However\, FL has three main limitations: First\, the parameter
 server (PS)\, which aggregates the local models that are trained using
 local user data\, is typically far from users. The large distance may
 burden the path links between the PS and local nodes\, thereby increasing
 the consumption of the network and computing resources. Second\, user
 device resources are limited \, but this aspect is not considered in the
 training of the local model and transmission of the model parameters.
 Third\, the PS-side links tend to become highly loaded as the number of
 participating clients increases. The links become congested owing to the
 large size of model parameters.\nOne of the kind in Federated Learning\,
 opportunistic federated learning\, in which individual devices belonging to
 different users seek to learn robust models that are personalized to their
 user’s own experiences. However\, instead of learning in isolation\,
 these models opportunistically incorporate the learned experiences of other
 devices they encounter opportunistically.
CATEGORIES:Practical Networks,Working Group,Youtube
LOCATION:Salle 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=Salle 4B01:geo:0,0
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TZID:Europe/Paris
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DTSTART:20230326T030000
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