FEDEMB: A VERTICAL AND HYBRID FEDERATED LEARNING ALGORITHM USING NETWORK AND FEATURE EMBEDDING AGGREGATION

FEDEMB: A VERTICAL AND HYBRID FEDERATED LEARNING ALGORITHM USING NETWORK AND FEATURE EMBEDDING AGGREGATION

Federated learning (FL) is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. The learning scheme may be horizontal, vertical or hybrid (both vertical and horizontal). Most existing research work with d...

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Bibliographic Details
Main Authors: Fanfei Meng, Lele Zhang, Yu Chen, Yuxin Wang
Format: Article
Language:English
Published: University of Kragujevac 2024-06-01
Series:Proceedings on Engineering Sciences
Subjects:
federated learning
differential privacy
vertical and hybrid federated learning
Online Access:https://pesjournal.net/journal/v6-n2/17.pdf

Internet

https://pesjournal.net/journal/v6-n2/17.pdf

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