R Neelaveni

Knee osteoarthritis diagnosis draws on multimodal data scattered across institutions, and centralizing it is both costly and privacy-risky. An asynchronous federated scheme first fuses structured records, semi-structured files and imaging into a queryable data table, then has ten simulated institutions compute gradients on vertically partitioned feature columns, with Bulyan aggregation and adaptive differential privacy filtering malicious updates on the server. On 5,580 knee osteoarthritis cases it reports 99.64% accuracy, 0.998 AUC, 285 MB communication overhead and a 1.8% attack success rate.

Knee osteoarthritis diagnosis draws on multimodal data scattered across institutions, and centralizing it is both costly and privacy-risky. An asynchronous federated scheme first fuses structured records, semi-structured files and imaging into a queryable data table, then has ten simulated institutions compute gradients on vertically partitioned feature columns, with Bulyan aggregation and adaptive differential privacy filtering malicious updates on the server. On 5,580 knee osteoarthritis cases it reports 99.64% accuracy, 0.998 AUC, 285 MB communication overhead and a 1.8% attack success rate.