Federated SDK

The core library that executes on-device model training. It loads the initial global model, performs stochastic gradient descent (SGD) or other optimization algorithms on the local dataset, and produces a model update (e.g., gradient tensors or weight deltas). This engine must be highly efficient to manage compute and memory constraints.

• Key Functions: Model loading, forward/backward propagation, optimizer step, checkpointing.
• Optimizations: Often includes support for quantized training and sparse updates to reduce resource consumption.

Manages all encrypted network interactions with the central Federated Learning Orchestrator. It handles protocol-specific communication for:

• Task Download: Receiving the global model and training configuration.
• Update Upload: Transmitting the computed model update or gradients.
• Authentication & Authorization: Proving device identity and permissions using certificates or tokens.

This component ensures end-to-end encryption (e.g., TLS) and may implement application-layer security for secure aggregation protocols.

Provides a standardized interface for the SDK to access the device's local training data while enforcing privacy-by-design. It does not export raw data.

• Responsibilities: Data loading, preprocessing, and partitioning into training batches.
• Privacy Enforcement: Ensures the training loop only accesses permitted, on-device data stores.
• Sampling Strategies: Can implement strategies for handling non-IID data distributions, such as stratified sampling, to improve local model quality.

A lightweight telemetry module that profiles the device's operational context to enable intelligent participation. It monitors:

• Compute State: CPU/GPU/NPU utilization and availability.
• Power Profile: Battery level and charging status.
• Network Conditions: Connectivity type (Wi-Fi, cellular), bandwidth, and data cost.

This data can be reported to the orchestrator's Client Selection Module to ensure training only occurs during favorable conditions, preserving user experience and device health.

Integrates privacy-preserving techniques directly into the local training process. This is where formal guarantees are applied before any data leaves the device.

• Differential Privacy: Injects calibrated noise into gradients or applies gradient clipping.
• Secure Multi-Party Computation (SMPC) Client: Participates in cryptographic protocols for secure aggregation.
• Local Anonymization: Applies local transformations or synthetic data generation where supported.

This component is critical for regulated industries like healthcare and finance.

Orchestrates the local execution of the federated learning round. It manages the state machine for the device's participation.

• Workflow: IDLE → TASK_RECEIVED → TRAINING → UPDATE_READY → UPLOADING → IDLE.
• Fault Tolerance: Handles interruptions (e.g., app backgrounding, network loss) by saving checkpoints and supporting resumable training.
• Configuration: Applies hyperparameters and training rules sent from the orchestrator's Configuration Manager.

The SDK must manage all network interactions with the Federated Learning Orchestrator. This involves implementing robust, encrypted communication protocols (e.g., gRPC over TLS) for reliable transmission of model updates and configurations. It must handle intermittent connectivity, automatic retries with exponential backoff, and efficient serialization of model weights (e.g., using protocol buffers). Bandwidth optimization techniques, such as model compression and delta updates, are critical for mobile or metered networks.

The SDK provides the core libraries to execute the on-device training loop. It must:

• Load the global model received from the orchestrator.
• Integrate seamlessly with the device's native machine learning frameworks (e.g., TensorFlow Lite, PyTorch Mobile, Core ML).
• Provide a standardized API for the application to feed local, private data into the training process.
• Execute federated averaging locally, applying optimizers like SGD or Adam on the client's dataset for a specified number of epochs, while managing device resources to prevent overheating or battery drain.

A primary challenge is abstracting away massive variability across the federated network. The SDK must:

• Dynamically profile device capabilities (CPU/GPU/Neural Processing Unit, memory, battery).
• Adapt training workload (batch size, number of local epochs) to match available resources.
• Handle diverse operating systems (Android, iOS, Linux, RTOS) and hardware architectures (ARM, x86).
• Provide fallback execution paths for devices without dedicated ML accelerators, ensuring broad participation.

The SDK is the enforcement point for privacy guarantees. It must integrate cryptographic and statistical primitives to ensure data never leaves the device in raw form. Key requirements include:

• Applying differential privacy mechanisms (e.g., gradient clipping and noise addition) to local updates before transmission.
• Participating in secure aggregation protocols (e.g., using secure multi-party computation) coordinated by the orchestrator, where the individual update is masked and only the sum is revealed.
• Enforcing local data access controls and sandboxing to prevent unauthorized reads by the host application.

The SDK must manage the complete lifecycle of a federated learning task on the device. This involves:

• Reliable checkpointing of the local model state to survive app closures or device reboots.
• Version compatibility between the SDK and the orchestrator's protocols and model formats.
• Graceful handling of task cancellation, model rollbacks, and configuration updates sent mid-round.
• Reporting training progress, success/failure status, and diagnostic telemetry back to the orchestrator's Resource Monitor.

For deployment on edge devices and in TinyML contexts, the SDK must have an extremely small binary size and minimal runtime overhead. Design challenges include:

• Stripping unused operators and kernels from bundled ML frameworks.
• Implementing efficient on-device model compression techniques like quantization-aware training.
• Avoiding garbage collection pauses and ensuring predictable memory usage.
• The SDK's own compute and energy consumption must be negligible compared to the core training task to ensure user acceptance.

The central server-side platform that manages the entire federated learning job lifecycle. It is responsible for:

• Client coordination: Registering devices and dispatching training tasks.
• Aggregation management: Running algorithms like Federated Averaging.
• Job scheduling & monitoring: Tracking round progress and model convergence. This orchestrator provides the APIs and interfaces that the Federated SDK on each client communicates with to participate in training.

The core algorithmic engine within the orchestrator that combines client updates. Its primary function is to execute the aggregation algorithm (e.g., weighted averaging) on the model updates received from the Federated SDKs. It must handle:

• Update validation: Checking for malformed or adversarial contributions.
• Weighted averaging: Applying appropriate weights based on client data sample counts.
• Model update generation: Producing a new global model for the next round.

The orchestrator component that maintains the state and metadata for all participating devices. It acts as the system of record for the federation, handling:

• Device profiling: Tracking compute capability, network status, and data statistics.
• Authentication & authorization: Verifying the identity of devices running the Federated SDK.
• Lifecycle state: Managing device availability (online/offline) and eligibility for training rounds. The Client Manager provides the participant list for the Client Selection Module.

A specialized service that coordinates cryptographic protocols to ensure the central server cannot inspect individual client updates. It works in tandem with the Federated SDK, which encrypts its model update. This component:

• Coordinates multi-party computation: Manages the cryptographic keys and steps among clients.
• Performs aggregation on ciphertext: Combines encrypted updates to produce an encrypted aggregate.
• Reveals only the final result: Decrypts the aggregated model update, never individual contributions. This provides a strong privacy guarantee beyond what basic Federated Averaging offers.

The single entry point for all communication between client devices (running the Federated SDK) and the orchestrator. It handles the protocol translation and network management, including:

• Request routing: Directing calls to the appropriate backend service (e.g., aggregator, client manager).
• Authentication & rate limiting: Protecting backend services from unauthorized or excessive requests.
• Protocol adaptation: Supporting different communication protocols (HTTP/gRPC) for diverse edge devices. The Federated SDK is configured to communicate directly with this gateway.

The centralized version control system for models within the federated learning system. It stores:

• Global model artifacts: Each version of the aggregated model produced by the Central Aggregator.
• Model metadata: Training configuration, performance metrics, and lineage data.
• Client model checkpoints: Snapshots for fault recovery. The Federated SDK downloads the initial model from the registry, and the orchestrator publishes new global models back to it after each aggregation round.