Privacy preserving discovery of failure signatures across heterogeneous wearable devices
Keywords:
wearable devices, federated learning, failure signature discovery, privacy preservation, reliability analyticsAbstract
Large-scale wearable ecosystems generate vast volumes of diagnostic data that contain valuable information for reliability analysis. However, centralized collection of device logs often raises privacy concerns and bandwidth limitations. This study proposes a privacy-preserving framework for discovering failure signatures across heterogeneous wearable devices. The proposed approach employs federated representation learning combined with secure aggregation to extract shared failure patterns without transferring raw diagnostic logs. Each device locally learns compact log embeddings that capture temporal event relationships, which are then aggregated through a privacy-preserving clustering mechanism. The framework was evaluated on a dataset containing 243 million diagnostic events collected from 68,432 wearable devices across four product categories, including smartwatches, health trackers, AR glasses, and environmental monitoring wearables. Experimental results show that the proposed method identifies 84 distinct failure signatures while preserving 94.1% of clustering quality compared with centralized analysis. In addition, the approach reduces network communication overhead by 82.7% and ensures that no raw log records leave local devices. The findings demonstrate that privacy-preserving failure analysis can enable scalable reliability monitoring across distributed wearable systems.
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