Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
Researchers show that tokeniser choice significantly affects how much patient data can be reconstructed via gradient inversion in federated learning on radiology reports, offering practical design guidance for healthcare AI.
Summary written by editorial AI · Source link below
arXiv:2607.14205v1 Announce Type: cross Abstract: Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensitive information from shared model updates. The extent of this leakage for radiology reports, and the role of tokenizer design, remains unclear. We quantify gradient-based text reconstruction in FL and compare privacy risk across three tokenizers with the model architecture held fixed. Six FL clients
Editorial Analysis
Healthcare and other regulated sectors using federated learning must consider tokeniser design as a privacy-relevant architecture decision, not just a performance parameter.
Include tokeniser privacy-leakage analysis in the design review for any federated learning deployment handling sensitive text data.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
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