Federated and Differentially Private Learning for Root Cause Classification Across Heterogeneous Semiconductor Fabs

Abstract

Federated learning (FL) can train a shared model without pooling client records, but it does not by itself provide differential privacy (DP). We compare centralized training, local-only training, federated averaging (FedAvg), and client-level DP-FedAvg on a fully released synthetic benchmark of free-text root-cause-analysis (RCA) records from six simulated semiconductor fabs. The benchmark uses a fixed public vocabulary, 240 records per client, a 480-record in-distribution test set, a separately authored 480-record template-disjoint test set, and five training seeds. All methods receive 24 passes over their available training data. The reference classifier is an auditable multinomial softmax regression with 512 parameters. The DP mechanism assumes fixed-roster replace-one client adjacency, clips only private client updates, adds Gaussian noise calibrated to the sensitivity of the weighted aggregate, and composes twelve releases with Rényi DP. In-distribution accuracy was 53.94% [53.60, 54.27] (mean, 95% confidence interval) for local-only training, 74.71% [73.05, 76.37] for FedAvg, and 91.88% [90.62, 93.13] for centralized training. Template-disjoint accuracy was 19–24% for all three. DP-FedAvg reached 65.92% [61.40, 70.44] at ε≈204 and fell to chance level at ε≈3.6 (δ=10−5). These results characterize a controlled benchmark and do not establish applicability to real fabs.