Quantum-Safe Key Exchanger
The Encryption Horizon: Visualising the Next Generation of
Quantum-Resilient Security.
Federated Learning Simulator
Decentralised AI &
The Data Problem
Federated Learning (FL) solves the critical problem of centralised data storage by bringing the model to the data, not the reverse.
This technique allows multiple decentralised edge devices to collaboratively train a shared AI model while keeping all raw data localised and private.
Model Updates,
Not Raw Data
Crucially, only the model updates (weights/gradients) are sent from the clients back to the central server for aggregation.
This process ensures the data privacy of individual users, as their sensitive information never leaves their local device.
Secure Aggregation
and Defense
The central server uses Federated Averaging (FedAvg) to combine all the updates into a single, improved global model.
Techniques like Differential Privacy can be simulated, adding noise to the updates to further protect against reverse-engineering attacks by malicious actors.
