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The feasibility of federated learning is highly constrained by theserver-clients infrastructure in terms of network communication. Most newlylaunched smartphones and IoT devices are equipped with GPUs or sufficientcomputing hardware to run powerful AI models. However, in case of the originalsynchronous federated learning, client devices suffer waiting times and regularcommunication between clients and server is required. This implies moresensitivity to local model training times and irregular or missed updates,hence, less or limited scalability to large numbers of clients and convergencerates measured in real time will suffer. We propose a new algorithm forasynchronous federated learning which eliminates waiting times and reducesoverall network communication - we provide rigorous theoretical analysis forstrongly convex objective functions and provide simulation results. By addingGaussian noise we show how our algorithm can be made differentially private --new theorems show how the aggregated added Gaussian noise is significantlyreduced.

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