  The performance of Federated Learning (FL) hinges on the effectiveness of
utilizing knowledge from distributed datasets. Traditional FL methods adopt an
aggregate-then-adapt framework, where clients update local models based on a
global model aggregated by the server from the previous training round. This
process can cause client drift, especially with significant cross-client data
heterogeneity, impacting model performance and convergence of the FL algorithm.
To address these challenges, we introduce FedAF, a novel aggregation-free FL
algorithm. In this framework, clients collaboratively learn condensed data by
leveraging peer knowledge, the server subsequently trains the global model
using the condensed data and soft labels received from the clients. FedAF
inherently avoids the issue of client drift, enhances the quality of condensed
data amid notable data heterogeneity, and improves the global model
performance. Extensive numerical studies on several popular benchmark datasets
show FedAF surpasses various state-of-the-art FL algorithms in handling
label-skew and feature-skew data heterogeneity, leading to superior global
model accuracy and faster convergence.
