  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 poisoning in FL.
FedTruth doesn't assume specific data distributions nor requires a benign root
dataset. It estimates a global model update with dynamic aggregation weights,
considering contributions from all benign clients. Empirical studies
demonstrate FedTruth's efficacy in mitigating the impacts of poisoned updates
from both Byzantine and backdoor attacks.
