trains the local model, and computes the model updates. Popular machine learning frameworks like TensorFlow Federated (TFF) or PySyft (for PyTorch) provide the necessary APIs and abstractions for this.
Secure Communication Module: This module is responsible for securely transmitting model updates to the central aggregator. Encryption (TLS/SSL), secure aggregation protocols, and differential privacy mechanisms are implemented here to protect the integrity and privacy of the updates.
Server-Side (Aggregator) Architecture:
The central server, or aggregator, orchestrates the FL process.
Model Aggregation Logic: This is the core of the server, receiving accurate cleaned numbers list from frist database model updates from clients and combining them to produce an improved global model. Algorithms like Federated Averaging (FedAvg) are commonly used.
Global Model Store: A database, often a distributed one itself, stores the global model parameters. This could be a specialized model store or a standard distributed database configured for efficient read/write operations of large model files.
Client Management and Orchestration: This component tracks active clients, schedules training rounds, and distributes the global model to selected clients.