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UID:860@lincs.fr
DTSTART;TZID=Europe/Paris:20241206T110000
DTEND;TZID=Europe/Paris:20241206T120000
DTSTAMP:20241216T152845Z
URL:https://www.lincs.fr/events/heroes-lightweight-federated-learning-with
 -neural-composition-and-adaptive-local-update-in-heterogeneous-edge-networ
 ks/
SUMMARY:Heroes: Lightweight Federated Learning with Neural Composition and
 Adaptive Local Update in Heterogeneous Edge Networks
DESCRIPTION:Federated Learning (FL) enables distributed clients to
 collaboratively train models without exposing their private data. However\,
 it is difficult to implement efficient FL due to limited resources. Most
 existing works compress the transmitted gradients or prune the global model
 to reduce the resource cost\, but leave the compressed or pruned parameters
 under-optimized\, which degrades the training performance. To address this
 issue\, the neural composition technique constructs size-adjustable models
 by composing low-rank tensors\, allowing every parameter in the global
 model to learn the knowledge from all clients. Nevertheless\, some tensors
 can only be optimized by a small fraction of clients\, thus the global
 model may get insufficient training\, leading to a long completion time\,
 especially in heterogeneous edge scenarios. To this end\, we enhance the
 neural composition technique\, enabling all parameters to be fully trained.
 Further\, we propose a lightweight FL framework\, called Heroes\, with
 enhanced neural composition and adaptive local update. A greedy-based
 algorithm is designed to adaptively assign the proper tensors and local
 update frequencies for participating clients according to their
 heterogeneous capabilities and resource budgets. Extensive experiments
 demonstrate that Heroes can reduce traffic consumption by about 72.05% and
 provide up to 2.97× speedup compared to the
 baselines.\n\n\nAuthors: Jiaming Yan\; Jianchun Liu\; Shilong
 Wang\; Hongli Xu\; Haifeng Liu\; Jianhua Zhou\n\n\nPublished in: IEEE
 INFOCOM 2024 - IEEE Conference on Computer Communications
CATEGORIES:Practical Networks,Working Group,Youtube
LOCATION:Amphi 6\, 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=Amphi 6:geo:0,0
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TZID:Europe/Paris
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DTSTART:20241027T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
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