Federated Learning for VVO

Local VVO models train directly on substation edge processors using raw feeder telemetry. Only encrypted model weight updates—not raw voltage or load data—are transmitted to the central aggregation server, preserving operational privacy while enabling collaborative learning across multiple feeders.

Mathematical noise is injected into model updates before transmission, providing provable privacy bounds against inference attacks. Key techniques include:

• Gaussian noise injection into gradient vectors
• Privacy budget tracking (ε, δ parameters)
• Clipping thresholds to bound individual contribution sensitivity This ensures even the aggregator cannot reconstruct individual feeder load profiles.

The FedAvg algorithm reduces bandwidth requirements by performing multiple local stochastic gradient descent steps before transmitting compressed updates. Advanced strategies include:

• Gradient sparsification to transmit only top-k significant weights
• Quantization of weight updates to 8-bit precision
• Periodic synchronization rather than continuous streaming These techniques reduce uplink traffic by up to 100x compared to raw data centralization.

Each feeder exhibits unique topological and load characteristics—varying phase imbalances, distributed generation penetration, and capacitor bank configurations. Federated learning accommodates this statistical heterogeneity through:

• Personalized local fine-tuning after global aggregation
• FedProx regularization to stabilize training across non-identical data distributions
• Clustered federated learning grouping feeders with similar impedance profiles This prevents the global model from averaging away critical local voltage behaviors.

Unlike centralized SCADA-dependent VVO, federated architectures maintain autonomous local control during communication failures. Each edge node retains a fully functional local model trained on its own feeder data, supplemented by the most recent global aggregation. When connectivity is restored, the node participates in the next asynchronous aggregation round without requiring full retraining.

The central aggregation server computes the weighted average of encrypted model updates without ever decrypting individual contributions. This is achieved through:

• Secure multi-party computation (MPC) protocols
• Homomorphic encryption allowing arithmetic on ciphertexts
• Trusted execution environments (hardware enclaves) for aggregation logic These cryptographic guarantees ensure that even a compromised aggregation server cannot extract individual feeder voltage sensitivity matrices or load profiles.