Federated Learning Enhances Candidate Data Privacy

Federated learning allows recruitment models to be trained across decentralized data sources without transferring sensitive candidate information to a central server. This ensures that personal data remains on the candidate’s device or within their organization, greatly reducing privacy risks while still enabling powerful, inclusive recruitment algorithms that learn from diverse datasets.