Federated Learning

A mathematical framework that injects calibrated statistical noise into model updates before they leave a utility's infrastructure. By bounding the influence of any single transformer's data, differential privacy provides a provable guarantee against membership inference attacks. The privacy budget, denoted by the parameter epsilon (ε), controls the trade-off between model accuracy and disclosure risk. Lower epsilon values enforce stricter privacy but may degrade the utility of fault predictions.

A cryptographic protocol ensuring that the central server can only compute the sum of encrypted model updates without ever inspecting individual contributions. Using multi-party computation (MPC) or homomorphic encryption, the aggregator remains blind to each substation's gradient vectors. This prevents an honest-but-curious coordinator from reverse-engineering sensitive load profiles or dissolved gas patterns from raw parameter updates.

A core challenge in federated transformer diagnostics where local datasets are not independent and identically distributed. One utility's fleet may consist primarily of aging shell-form transformers with corrosive sulfur issues, while another operates newer core-form units with predominantly thermal faults. This statistical heterogeneity causes local model objectives to diverge from the global optimum, requiring specialized algorithms like FedProx or SCAFFOLD to stabilize convergence.

Two architectural paradigms distinguished by data partitioning:

• Horizontal Federated Learning (HFL): Utilities share the same feature space (DGA gases, temperature) but have different transformer populations. This is the standard scenario for collaborative fault classification.
• Vertical Federated Learning (VFL): Entities hold different features for the same transformers—one has electrical test data, another has oil analysis. Entity alignment techniques match records without revealing identities before training split neural networks.

The foundational aggregation algorithm where each participating substation trains a local model on its own dissolved gas and thermal data, then transmits only the updated weights to a central server. The server computes a weighted average of these updates to produce the next global model. Communication rounds repeat until convergence. While simple, FedAvg can struggle with non-IID data distributions and straggler nodes with limited bandwidth.

Adversarial countermeasures protecting the global transformer fault model from malicious utilities submitting corrupted updates designed to degrade performance or implant backdoors. Techniques include:

• Norm clipping: Bounding the magnitude of weight updates
• Krum aggregation: Selecting the update most similar to its peers, rejecting outliers
• Differential privacy auditing: Detecting statistical anomalies in contribution patterns These safeguards are critical when federated learning spans multiple, potentially competing, grid operators.