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UID:354@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250516T140000
DTEND;TZID=Europe/Paris:20250516T140000
DTSTAMP:20260828T093800Z
URL:https://isdm.umontpellier.fr/events/unified-breakdown-analysis-for-byz
 antine-robust-gossip-3/
SUMMARY:Unified Breakdown Analysis for Byzantine Robust Gossip
DESCRIPTION:Room 02.249\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice - Hadrien Hendrikx (Inria)\n\nIn 
 decentralized machine learning\, different devices communicate in a peer-t
 o-peer manner to collaboratively learn from each other&#x27\;s data. Such 
 approaches are vulnerable to misbehaving (or Byzantine) devices. We introd
 uce F-RG\, a general framework for building robust decentralized algorithm
 s with guarantees arising from robust-sum-like aggregation rules F. We the
 n investigate the notion of breakdown point\, and show an upper bound on t
 he number of adversaries that decentralized algorithms can tolerate. We in
 troduce a practical robust aggregation rule\, coined CSours\, such that CS
 ours-RG has a near-optimal breakdown. Other choices of aggregation rules l
 ead to existing algorithms such as ClippedGossip or NNA. We give experimen
 tal evidence to validate the effectiveness of CSours-RG and highlight the 
 gap with NNA\, in particular against a novel attack tailored to decentrali
 zed communications\n\nMachine Learning in Montpellier\, Theory &amp\; Prac
 tice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Room 02.249 Building 5\, St Priest campus\, Montpellier\, 34000\, 
 France
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DTSTART:20250330T030000
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