Federated Data Sanitization Defense

Federated data sanitization defense focuses on securing federated learning systems against malicious data poisoning attacks, primarily in sensitive domains like healthcare. Current research emphasizes developing robust methods for identifying and filtering poisoned data, often employing techniques like federated clustering and gradient-based approaches to detect anomalies in aggregated updates, even with limited labeled data. These advancements aim to improve the trustworthiness and reliability of federated learning models by enhancing data integrity and privacy, thereby enabling wider adoption in applications requiring high security and data confidentiality.