FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

This paper presents FedCVESA, a technique that enables extraction of training data from federated learning systems through correlation value encoding and segmented aggregation methods. The research demonstrates a potential privacy vulnerability in federated learning architectures commonly used in regulated industries, where data is supposed to remain decentralized and protected.

This paper presents FedCVESA, a technique that enables extraction of training data from federated learning systems through correlation value encoding and segmented aggregation methods. The research demonstrates a potential privacy vulnerability in federated learning architectures commonly used in regulated industries, where data is supposed to remain decentralized and protected.

High-quality data pipelines remain the most consistent bottleneck in enterprise AI maturity. QuettaMinds helps clients close the gap between raw data assets and production-ready AI inputs.