Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning

Federated Learning (FL) has been proposed as a privacy-preserving solution for distributed machine learning, particularly in heterogeneous FL settings where clients have varying computational capabilities and thus train models with different complexities compared to the server’s model. Howeve...

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Bibliografski detalji

Glavni autori:	Gergely D. Nemeth, Miguel Angel Lozano, Novi Quadrianto, Nuria Oliver
Format:	Artigo
Jezik:	Inglês
Izdano:	IEEE 2025-01-01
Serija:	IEEE Access
Teme:	Federated learning membership inference attack model complexity privacy
Online pristup:	https://ieeexplore.ieee.org/document/10906485/
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