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pro vyhledávání: '"Lin, Frank P."'
While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from the communicated model parameters. In this work, we propose \underline{H}ierarchical \underline{F}ederated Learning
Externí odkaz:
http://arxiv.org/abs/2401.11592
Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) to improve the efficiency of distributed machine learning (ML) model t
Externí odkaz:
http://arxiv.org/abs/2303.12414
Autor:
Nickel, David, Lin, Frank Po-Chen, Hosseinalipour, Seyyedali, Michelusi, Nicolo, Brinton, Christopher G.
Federated learning (FL) has emerged as a popular technique for distributing machine learning across wireless edge devices. We examine FL under two salient properties of contemporary networks: device-server communication delays and device computation
Externí odkaz:
http://arxiv.org/abs/2112.13926
Autor:
Lin, Frank Po-Chen, Hosseinalipour, Seyyedali, Azam, Sheikh Shams, Brinton, Christopher G., Michelusi, Nicolò
Federated learning has emerged as a popular technique for distributing model training across the network edge. Its learning architecture is conventionally a star topology between the devices and a central server. In this paper, we propose two timesca
Externí odkaz:
http://arxiv.org/abs/2109.03350
Publikováno v:
J Sci Comput 93, 12 (2022)
This article presents an immersed virtual element method for solving a class of interface problems that combines the advantages of both body-fitted mesh methods and unfitted mesh methods. A background body-fitted mesh is generated initially. On those
Externí odkaz:
http://arxiv.org/abs/2108.00619
Autor:
Lin, Frank Po-Chen, Hosseinalipour, Seyyedali, Azam, Sheikh Shams, Brinton, Christopher G., Michelusi, Nicolo
Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid federated learning (TT-HF), a semi-decentralized learning architectur
Externí odkaz:
http://arxiv.org/abs/2103.10481
Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks. This work addresses an important consideration of federated learning at the network edge: commu
Externí odkaz:
http://arxiv.org/abs/2008.09323
A sofic approximation to a countable group is a sequence of partial actions on finite sets that asymptotically approximates the action of the group on itself by left-translations. A group is sofic if it admits a sofic approximation. Sofic entropy the
Externí odkaz:
http://arxiv.org/abs/1911.08272
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