crosssilo

Cross-silo, often written crosssilo in some texts, refers to a setting in federated learning where multiple organizations with distinct, centralized data silos collaborate to train a shared machine learning model without exchanging raw data. Compared with cross-device federated learning, which involves many devices with personal data, cross-silo FL typically involves a small number of entities, each holding large, structured datasets such as electronic health records or financial transactions.

In a cross-silo arrangement, participating organizations run local training on their data and periodically share model updates (for example, parameter deltas or gradients) with a coordinating server or among each other. Updates are aggregated to form a new global model, with privacy-preserving techniques like secure aggregation or differential privacy used to reduce leakage risk. Data remains within each organization’s premises to comply with regulatory and competitive constraints.

Benefits include leveraging complementary data to improve model performance, while preserving data sovereignty and reducing data transfer costs. Typical use cases include healthcare, banking, insurance, and manufacturing where data sensitivity and governance requirements are strong.

Key challenges include heterogeneity of data schemas and distributions across silos (non-IID data), aligning incentives and governance between organizations, regulatory compliance, and operational overhead. Technical challenges encompass secure aggregation, communication efficiency, model divergence, and debugging in a distributed setting.

Researchers and practitioners emphasize clear data governance, robust privacy controls, auditing, and careful evaluation to ensure fair performance across silos and to avoid leakage of sensitive information via model updates.