… Federated Learning with Vision Transformers) performs especially well in settings with most
heterogeneous … 19] increasing significantly as heterogeneity increases. To understand these …

… privacy-preserving, distributed learning with a network of … to data heterogeneity in FL with
a focus on local learning gen… , FedAlign, to overcome data heterogeneity and the pitfalls of …

… Systems heterogeneity in federated learning. Devices may vary in terms of network …
Therefore, federated training methods must tolerate heterogeneous systems environments and low …

… Federated Learning (FL) [2] is a machine learning approach … of a central FL server to
learn a global model without ever … , is dealing with the intrinsic heterogeneity of real-world …

Federated learning (FL) has drawn increasing attention owing to its … However, practical FL
typically faces the heterogeneity of … Heterogeneous Federated Learning (HFL) is much more …

… types of heterogeneity can slow down the learning process, while state heterogeneity is often
… : (1) we comprehensively consider hardware heterogeneity and state heterogeneity in the …

… model in federated learning, we perform an experimental study on heterogeneous local … 10
with 10 clients which is a standard federated learning benchmark, and a convolutional neural …

… arise in Federated Learning particularly in the presence of heterogeneous data distribution
(ie, … , we solve the robust heterogeneous Federated Learning problem optimally; in particular …

… Cross-Correlation and Continual Learning). The overview of FCCL … federated learning.
Through leveraging unlabeled public data and adopting self-supervised learning, heterogeneous …

… influence of heterogeneity on the effect of federated learning. … three heterogeneous challenges
faced by federated learning … heterogeneity, data heterogeneity and model heterogeneity, …