A Verifiable, Privacy-Preserving, and Poisoning Attack-Resilient Federated Learning Framework

A Verifiable, Privacy-Preserving, and Poisoning Attack-Resilient Federated Learning Framework

Federated learning is the on-device, collaborative training of a global model that can be utilized to support the privacy preservation of participants’ local data. In federated learning, there are challenges to model training regarding privacy preservation, security, resilience, and integrity. For e...

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Bibliographic Details
Main Authors: Washington Enyinna Mbonu, Carsten Maple, Gregory Epiphaniou, Christo Panchev
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Big Data and Cognitive Computing
Subjects:
privacy preservation
poisoning attack
correctness of aggregation
zero trust
federated learning
Online Access:https://www.mdpi.com/2504-2289/9/4/85
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