FL-OA outsources model-update auditing to a third-party server with a root dataset, adds a gradient ascent step and a correction term to local training, and reduces audit dimension by extracting critical parameters.
The impact of adversarial attacks on federated learning: A survey,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devices
FL-OA outsources model-update auditing to a third-party server with a root dataset, adds a gradient ascent step and a correction term to local training, and reduces audit dimension by extracting critical parameters.