Study finds privacy risks in federated learning for radiology reports; analyzing tokenizer-driven leakage via gradient inversion
Read the original at arxiv.org→arXiv:2607.14205v1 Announce Type: new Abstract: Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensitive information...
Original headline: "Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks"