  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. We propose a new algorithm for
asynchronous federated learning which eliminates waiting times and reduces
overall network communication - we provide rigorous theoretical analysis for
strongly convex objective functions and provide simulation results. By adding
Gaussian noise we show how our algorithm can be made differentially private --
new theorems show how the aggregated added Gaussian noise is significantly
reduced.

Machine Learning;Cryptography and Security;Optimization and Control;Machine Learning