Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …

We investigate the hypothesis that exploring Federated Learning (FL) aggregation methods
can enhance training processes within FL frameworks, particularly in resource-constrained …

There are two categories of methods in Federated Learning (FL) for joint training across
multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) …

Local SGD is a popular optimization method in distributed learning, often outperforming mini-
batch SGD. Despite this practical success, proving the efficiency of local SGD has been …

Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive
applications without sacrificing the sensitive private information of clients. However, the data …

We propose and study several server-extrapolation strategies for enhancing the theoretical
and empirical convergence properties of the popular federated learning optimizer FedProx …

Federated learning aims to train models collaboratively across different clients without
sharing data for privacy considerations. However, one major challenge for this learning …

Federated Learning (FL) suffers from severe performance degradation due to the data
heterogeneity among clients. Some existing work suggests that the fundamental reason is …

Federated Learning (FL) is an emerging collaborative machine learning framework where
multiple clients train the global model without sharing their own datasets. In FL, the model …

While providing machine learning model as a service to process users' inference requests,
online applications can periodically upgrade the model utilizing newly collected data …