  Federated learning (FL) is a powerful Machine Learning (ML) paradigm that
enables distributed clients to collaboratively learn a shared global model
while keeping the data on the original device, thereby preserving privacy. A
central challenge in FL is the effective aggregation of local model weights
from disparate and potentially unbalanced participating clients. Existing
methods often treat each client indiscriminately, applying a single proportion
to the entire local model. However, it is empirically advantageous for each
weight to be assigned a specific proportion. This paper introduces an
innovative Element-Wise Weights Aggregation Method for Federated Learning
(EWWA-FL) aimed at optimizing learning performance and accelerating convergence
speed. Unlike traditional FL approaches, EWWA-FL aggregates local weights to
the global model at the level of individual elements, thereby allowing each
participating client to make element-wise contributions to the learning
process. By taking into account the unique dataset characteristics of each
client, EWWA-FL enhances the robustness of the global model to different
datasets while also achieving rapid convergence. The method is flexible enough
to employ various weighting strategies. Through comprehensive experiments, we
demonstrate the advanced capabilities of EWWA-FL, showing significant
improvements in both accuracy and convergence speed across a range of backbones
and benchmarks.
