  Federated Learning allows training of data stored in distributed devices
without the need for centralizing training data, thereby maintaining data
privacy. Addressing the ability to handle data heterogeneity (non-identical and
independent distribution or non-IID) is a key enabler for the wider deployment
of Federated Learning. In this paper, we propose a novel Divide-and-Conquer
training methodology that enables the use of the popular FedAvg aggregation
algorithm by overcoming the acknowledged FedAvg limitations in non-IID
environments. We propose a novel use of Cosine-distance based Weight Divergence
metric to determine the exact point where a Deep Learning network can be
divided into class agnostic initial layers and class-specific deep layers for
performing a Divide and Conquer training. We show that the methodology achieves
trained model accuracy at par (and in certain cases exceeding) with numbers
achieved by state-of-the-art Aggregation algorithms like FedProx, FedMA, etc.
Also, we show that this methodology leads to compute and bandwidth
optimizations under certain documented conditions.
