What is cross-silo federated learning?

Cross-silo federated learning is a machine learning approach where multiple organizations or large entities (silos) collaboratively train a shared model without directly sharing their raw data. Each silo maintains control over its local dataset, and only model updates—such as gradients or parameters—are exchanged during training. This method prioritizes data privacy, making it suitable for scenarios where legal, ethical, or technical constraints prevent data consolidation. Unlike cross-device federated learning, which involves many unreliable devices (e.g., smartphones), cross-silo typically involves fewer, more stable participants, like hospitals, banks, or research institutions, with robust computational resources. The process is coordinated by a central server or through peer-to-peer communication, aggregating updates to refine the global model iteratively.

A common example is healthcare. Hospitals might collaborate to train a diagnostic model using patient data, but regulations like HIPAA prevent sharing sensitive records. Each hospital trains the model locally on its data, then sends encrypted updates to a central server. The server combines these updates into a global model, which is redistributed for further training. Similarly, financial institutions could use cross-silo federated learning to detect fraud. Banks might train a shared model on transaction patterns without exposing customer data. Another use case is industrial predictive maintenance: manufacturing companies with separate factories could pool insights from equipment sensors to build a model predicting machine failures, while keeping each factory’s operational data private.

Key technical challenges include handling non-IID (non-independent and identically distributed) data across silos. For instance, one hospital’s dataset might focus on pediatric cases, while another specializes in geriatrics, leading to skewed model performance. Techniques like Federated Averaging (FedAvg) adjust aggregation to account for data imbalances. Communication efficiency is also critical—frequent model exchanges between silos with large datasets can be costly. Frameworks like TensorFlow Federated or PySyft provide tools to manage these workflows. Security measures, such as differential privacy or secure multiparty computation, are often added to prevent potential data leakage from model updates. Developers implementing cross-silo systems must also address trust and coordination, ensuring all parties agree on protocols for updates, aggregation, and model ownership.