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Communication costs within Federated learning hinder the system scalabilityfor reaching more data from more clients. The proposed FL adopts ahub-and-spoke network topology. All clients communicate through the centralserver. Hence, reducing communication overheads via techniques such as datacompression has been proposed to mitigate this issue. Another challenge offederated learning is unbalanced data distribution, data on each client are notindependent and identically distributed (non-IID) in a typical federatedlearning setting. In this paper, we proposed a new compression compensationscheme called Global Momentum Fusion (GMF) which reduces communicationoverheads between FL clients and the server and maintains comparable modelaccuracy in the presence of non-IID data. GitHub repository:https://github.com/tony92151/global-momentum-fusion-fl

Improving Federated Learning Communication Efficiency with Global Momentum Fusion for Gradient Compression Schemes