Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise

Understand

The feasibility of federated learning is highly constrained by the server-clients infrastructure in terms of network communication.

• Most newly launched smartphones and IoT devices are equipped with GPUs or sufficient computing hardware to run powerful AI models.
• However, in case of the original synchronous federated learning, client devices suffer waiting times and regular communication between clients and server is required.
• This implies more sensitivity to local model training times and irregular or missed updates, hence, less or limited scalability to large numbers of clients and convergence rates measured in real time will suffer.