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BEGIN:VEVENT
UID:792@lincs.fr
DTSTART;TZID=Europe/Paris:20231122T103000
DTEND;TZID=Europe/Paris:20231122T113000
DTSTAMP:20231201T111546Z
URL:https://www.lincs.fr/events/gossip-learning-with-linear-models-on-full
 y-distributed-data/
SUMMARY:Gossip learning with linear models on fully distributed data
DESCRIPTION:In the realm of distributed machine learning\, a novel approach
 called Gossip Learning\, was presented in 2013 by the paper "Gossip
 Learning with Linear Models on Fully Distributed Data"[1]. Gossip Learning
 introduces a decentralized communication framework among nodes\, allowing
 them to collaboratively train a machine learning model without the need for
 a central aggregator. This decentralized paradigm offers advantages in
 scalability\, fault tolerance\, and privacy preservation.\n\nThe
 presentation will delve into the key components of Gossip Learning\,
 highlighting the communication protocol that enable nodes to exchange
 information in a gossip-like fashion\, the convergence properties of Gossip
 Learning and its resilience to node failures.\nAdditionally\, the
 presentation will discuss comparisons with traditional centralized learning
 approaches (and federated learning approaches).\n\nReferences:\n\n[1]
 Ormándi\, R.\, Heged?s\, I.\, &amp\; Jelasity\, M. (2013). Gossip learning
 with linear models on fully distributed data. Concurrency and Computation:
 Practice and Experience\, 25(4)\, 556-571.\nhttps://arxiv.org/abs/1109.1396
CATEGORIES:Practical Networks,Working Group,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
X-LIC-LOCATION:Europe/Paris
BEGIN:STANDARD
DTSTART:20231029T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
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