Model Poisoning Defense on Federated Learning: A Validation Based Approach | IBOOK.PUB

Model Poisoning Defense on Federated Learning: A Validation Based Approach

Federated learning is an improved distributed machine learning approach for privacy preservation. All clients collaboratively train the model using on-device data, and the centralized server only aggregates clients’ training results instead of collecting

PDF / 27,845,979 Bytes
458 Pages / 439.37 x 666.142 pts Page_size
3 Downloads / 214 Views

Recommend Documents

Data Poisoning Attacks Against Federated Learning Systems

216 40 48MB

A Scalable Mixture Model Based Defense Against Data Poisoning Attacks on Classifiers

200 38 35MB

A Principled Approach to Data Valuation for Federated Learning

198 8 27MB

A Medical Blockchain Privacy Protection Model Based on Mimicry Defense

149 51 78MB

Threats to Federated Learning

232 16 27MB

Blockchain-Based Participant Selection for Federated Learning

285 96 55MB

Catalysis on a Fractal Lattice: A Model for Poisoning

172 16 206KB

Federated Generative Adversarial Learning

207 66 65MB

A trusted recommendation scheme for privacy protection based on federated learning

206 80 1MB

Building ICU In-hospital Mortality Prediction Model with Federated Learning

171 84 27MB

Simulation and Learning A Model-Centered Approach

175 16 6MB

A Theoretical Study on Access Control Model in Federated Systems

166 58 391KB