Workflow Engine (Federated Learning)

The engine defines the federated learning process as a Directed Acyclic Graph (DAG), where nodes represent tasks (e.g., client selection, training, aggregation) and edges define dependencies. This provides:

• Deterministic sequencing of complex, multi-step workflows.
• Explicit error handling and retry logic for failed steps.
• Parallel execution of independent tasks, such as training on multiple clients simultaneously.
• Visualization and debugging of the entire training pipeline's state and data flow.

The engine maintains persistent state across federated rounds, tracking:

• Global model versioning and checkpointing.
• Client participation history and performance metrics.
• Aggregation results and convergence metrics.
• Privacy budget consumption for differential privacy. This statefulness is crucial for managing long-running jobs, enabling rollbacks, and providing auditable logs of the entire training lifecycle.

Engineers define a federated job using a high-level, declarative configuration (e.g., YAML, JSON). This specification abstracts the underlying orchestration complexity and includes:

• Model architecture and initialization parameters.
• Client selection strategy (random, resource-aware, stratified).
• Training hyperparameters (local epochs, batch size, optimizer).
• Aggregation algorithm (e.g., Federated Averaging, FedProx).
• Stopping criteria (target accuracy, round limit, convergence). The engine interprets this spec and dynamically generates the executable DAG.

The engine is designed to handle the inherent heterogeneity and unreliability of edge devices. Core mechanisms include:

• Adaptive scheduling based on real-time client resource profiles (battery, compute, network).
• Client dropout handling via timeouts and partial update aggregation.
• Checkpointing and recovery to resume jobs from the last valid state after server or client failures.
• Graceful degradation allowing training to proceed with a subset of available clients, ensuring job completion despite volatile conditions.

The workflow engine seamlessly integrates critical privacy and security components as first-class workflow steps:

• Secure Aggregation Orchestration: Coordinates cryptographic protocols (e.g., SecAgg) to mask individual client updates before they reach the aggregator.
• Differential Privacy Enforcement: Applies noise injection and gradient clipping as defined steps within the training loop to guarantee formal privacy bounds (e.g., (ε, δ)-DP).
• Compliance Gating: Includes validation steps that check operations against regulatory policies (e.g., data residency) before proceeding.

The engine provides comprehensive observability into the distributed training process, emitting structured telemetry for:

• System Metrics: Client connectivity rates, round duration, network bandwidth consumption, and server resource usage.
• Model Metrics: Global and client-specific loss, accuracy, and fairness scores tracked per round.
• Operational Events: Client selection, task dispatch, aggregation success/failure, and model deployment triggers. This data feeds dashboards and alerts, enabling DevOps teams to monitor health, debug issues, and optimize performance.