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UID:795@lincs.fr
DTSTART;TZID=Europe/Paris:20231213T103000
DTEND;TZID=Europe/Paris:20231213T113000
DTSTAMP:20240122T154502Z
URL:https://www.lincs.fr/events/ensemble-distillation-for-robust-model-fus
 ion-in-federated-learning/
SUMMARY:Ensemble distillation for robust model fusion in federated learning
DESCRIPTION:Federated Learning (FL) attempts to protect the privacy of the
 participants in the scheme by uploading locally-trained models from edge
 devices to a server\, where they get combined into a global model and
 redistributed to the edge devices. In the established FL literature\, this
 combination usually happens by averaging\, which appears to limit the
 accuracy of the global model and introduces challenges in applying the
 scheme to heterogeneous devices with different model architectures.\n\nThe
 paper we will present in this session\, "Ensemble distillation for robust
 model fusion in federated learning"\, introduces model fusion techniques
 based on knowledge distillation that enable the simultaneous use of various
 local model sizes / architectures. Distillation also drastically improves
 the global accuracy compared to average-based methods such as FedAVG -
 still without compromising users' data privacy. The proposed scheme\,
 FedDF\, is faster to train and requires fewer communication rounds than
 previously published FL techniques. We liked this paper because it
 introduces a couple of nifty ideas that might also be profitably applied in
 other FL scenarios.\n\nReference\n\nTao Lin\, Lingjing Kong\, Sebastian U.
 Stich\, and Martin Jaggi. Ensemble distillation for robust model fusion in
 federated learning. In Proceedings of the 34th International Conference on
 Neural Information Processing Systems\, NIPS’20\, pages 2351–2363\, Red
 Hook\, NY\, USA\, December 2020.
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
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DTSTART:20231029T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
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