FedIPR: Ownership Verification for Federated Deep Neural Network Models

Abstract

In federated learning, multiple clients collaboratively develop models upon their private data. However, IP risks including illegal copying, re-distribution, and free-riding threat the collaboratively built models in federated learning. To address IP infringement issues, in this chapter, we introduce a novel deep neural network ownership verification framework for secure federated learning that allows each client to embed and extract private watermarks in federated learning models for legitimate IPR. In the proposed FedIPR scheme, each client independently extracts the watermarks and claims ownership on the federated learning model while keep training data and watermark private.