API Gateway (Federated Learning)

The API Gateway acts as the single point of entry for all communication between the federated learning orchestrator (server) and the participating edge devices (clients). It abstracts the complexity of the backend orchestrator, presenting a stable, versioned API. This allows clients to interact with a consistent endpoint regardless of internal server architecture changes, simplifying client-side SDK development and deployment.

• Protocol Standardization: Clients may use different communication libraries or protocols (e.g., gRPC, HTTP/2, MQTT). The gateway translates these into a unified internal protocol for the orchestrator.
• Request Routing: It intelligently routes incoming requests—such as model download requests, update submissions, or heartbeat signals—to the correct backend service (e.g., the Model Registry, Round Coordinator, or Client Manager).

This function ensures that only legitimate, authorized clients and servers can participate in the federation. The gateway validates credentials and permissions for every request, forming the first line of defense for the private learning system.

• Client Authentication: Verifies device identity using certificates, API keys, or OAuth tokens before allowing participation in a training round.
• Role-Based Access Control (RBAC): Enforces policies determining which clients can access specific model versions or participate in certain federated jobs. It also controls administrative access to the orchestrator's configuration and monitoring endpoints.
• Mutual TLS (mTLS): Often implements mTLS to ensure both client and server identities are verified, preventing man-in-the-middle attacks during the exchange of sensitive model updates.

The gateway protects the orchestrator from being overwhelmed by managing the flow of requests from potentially millions of edge devices. It implements policies to ensure system stability and fair resource usage.

• Rate Limiting: Applies quotas to prevent any single client or group from monopolizing server resources, which is critical during peak participation periods.
• Load Balancing: Distributes incoming requests across multiple instances of backend services (like aggregators) to prevent bottlenecks and ensure high availability.
• Request Queuing & Throttling: Manages traffic spikes by queuing requests and releasing them at a sustainable pace, preventing server crashes during synchronous aggregation phases.

The gateway acts as a mediator, ensuring data exchanged between heterogeneous clients and the server is in the correct format, size, and structure. This decouples client implementations from the server's internal data schemas.

• Payload Validation: Checks that incoming model updates conform to expected tensor shapes, data types, and size limits before forwarding them to the Secure Aggregation Orchestrator. Malformed or suspicious updates can be rejected.
• Data Transformation: May compress model updates using techniques like quantization or pruning to reduce bandwidth. It can also add or strip metadata, or convert between serialization formats (e.g., Protobuf to JSON).
• Response Adaptation: Tailors server responses for different client types—for example, providing a lightweight model binary for a microcontroller versus a full checkpoint for a powerful edge server.

As the central communication hub, the gateway is the ideal point to collect system-wide metrics and logs, providing crucial visibility into the health and performance of the federated learning process.

• Metric Collection: Gathers data on request volumes, latency, error rates, client participation counts, and update sizes. This data feeds into the Convergence Monitor and Resource Monitor.
• Distributed Tracing: Injects and propagates trace identifiers across requests, allowing engineers to follow the journey of a single client's update through the entire system for debugging.
• Audit Logging: Records all access attempts and request metadata, providing an immutable log for the Audit Logger. This is essential for compliance, proving that only authorized actions were taken on sensitive model data.

The gateway coordinates with specialized privacy and security components to enforce protocols before data reaches the core aggregator. It acts as the traffic controller for privacy-enhancing technologies (PETs).

• Secure Aggregation Coordination: Can route client updates directly to a Secure Aggregation Orchestrator, ensuring encrypted updates are combined before the plaintext aggregate is revealed.
• Differential Privacy Enforcement: Integrates with a Differential Privacy Orchestrator to apply noise injection or gradient clipping to updates as they pass through, ensuring formal privacy guarantees are met before aggregation.
• Compliance Gateway: Works with a Compliance Checker to validate requests against data sovereignty rules (e.g., ensuring updates from certain geographic regions are processed within specific infrastructure).

The central software platform that manages the entire lifecycle of a federated learning job. It is the parent system that contains the API Gateway, Central Aggregator, Client Manager, and other modules. Its core responsibilities include:

• Job scheduling and workflow automation
• Global model versioning and lifecycle management
• System-wide configuration distribution
• Providing the administrative dashboard and control plane

The server-side algorithm engine responsible for combining client model updates to produce a new global model. It executes the core federated learning algorithm (e.g., Federated Averaging). The API Gateway routes encrypted updates to this component. Its functions are:

• Securely receiving model deltas or weights from clients
• Applying aggregation algorithms (e.g., weighted averaging)
• Producing the updated global model artifact
• Often integrated with Secure Aggregation protocols

The module that maintains the state and metadata for all participating edge devices or institutional clients. It works in tandem with the API Gateway, which handles the real-time communication. The Client Manager's role includes:

• Device registration, authentication, and profiling (compute, memory, network)
• Tracking client availability and health status
• Managing client-side software versions and dependencies
• Informing the Client Selection Module for each training round

The component that executes a single federated learning round from start to finish. It triggers the sequence of events that the API Gateway facilitates. A typical round involves:

1. Client Selection: Choosing a subset of available devices.
2. Task Dispatch: Sending the global model & training config via the API Gateway.
3. Update Collection: Receiving client updates through the API Gateway.
4. Aggregation Trigger: Passing the collected updates to the Central Aggregator.

A specialized coordinator for cryptographic protocols that ensure the server cannot inspect any individual client's model update. It often works alongside the API Gateway and Central Aggregator. Key protocols include:

• Multi-Party Computation (MPC): Clients secret-share their updates.
• Homomorphic Encryption: Allows computation on encrypted data.
• This component manages the cryptographic key exchange and coordinates the secure summation process before the plaintext aggregate is revealed.

Two primary architectural patterns for federation, which dictate API Gateway design. Cross-Silo involves a few reliable, powerful institutional servers (e.g., hospitals). Cross-Device involves millions of unstable, constrained edge devices (e.g., phones).

• Cross-Silo: API Gateway handles fewer, larger, more reliable connections; protocols may be heavier (gRPC, HTTPS).
• Cross-Device: API Gateway must be massively scalable, handle frequent dropouts, and use efficient protocols (e.g., HTTP/2, MQTT).