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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Hlavní autoři:	Gergely D. Nemeth, Miguel Angel Lozano, Novi Quadrianto, Nuria Oliver
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2025-01-01
Edice:	IEEE Access
Témata:	Federated learning membership inference attack model complexity privacy
On-line přístup:	https://ieeexplore.ieee.org/document/10906485/
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