  Federated learning has emerged as a new paradigm of collaborative machine
learning; however, it has also faced several challenges such as non-independent
and identically distributed(IID) data and high communication cost. To this end,
we propose a novel framework of IID and communication-aware group federated
learning that simultaneously maximizes both accuracy and communication speed by
grouping nodes based on data distributions and physical locations of the nodes.
Furthermore, we provide a formal convergence analysis and an efficient
optimization algorithm called FedAvg-IC. Experimental results show that,
compared with the state-of-the-art algorithms, FedAvg-IC improved the test
accuracy by up to 22.2% and simultaneously reduced the communication time to as
small as 12%.
