Nov. 20, 2023, 2:10 a.m. | Sheldon C. Ebron Jr., Kan Yang

cs.CR updates on

Federated Learning (FL) enables collaborative machine learning model training
across multiple parties without sharing raw data. However, FL's distributed
nature allows malicious clients to impact model training through Byzantine or
backdoor attacks, using erroneous model updates. Existing defenses measure the
deviation of each update from a 'ground-truth model update.' They often rely on
a benign root dataset on the server or use trimmed mean or median for clipping,
both methods having limitations.

We introduce FedTruth, a robust defense against model …

attacks backdoor backdoor attacks clients data defenses distributed federated federated learning framework impact machine machine learning malicious measure model training nature sharing training truth update updates

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