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KnFu: Effective Knowledge Fusion
March 19, 2024, 4:42 a.m. | S. Jamal Seyedmohammadi, S. Kawa Atapour, Jamshid Abouei, Arash Mohammadi
cs.LG updates on arXiv.org arxiv.org
Abstract: Federated Learning (FL) has emerged as a prominent alternative to the traditional centralized learning approach. Generally speaking, FL is a decentralized approach that allows for collaborative training of Machine Learning (ML) models across multiple local nodes, ensuring data privacy and security while leveraging diverse datasets. Conventional FL, however, is susceptible to gradient inversion attacks, restrictively enforces a uniform architecture on local models, and suffers from model heterogeneity (model drift) due to non-IID local datasets. To …
abstract arxiv collaborative cs.dc cs.lg data data privacy datasets decentralized diverse federated learning fusion however knowledge machine machine learning multiple nodes privacy privacy and security security speaking training type
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