  One of the key challenges in federated learning (FL) is local data
distribution heterogeneity across clients, which may cause inconsistent feature
spaces across clients. To address this issue, we propose a novel method FedFM,
which guides each client's features to match shared category-wise anchors
(landmarks in feature space). This method attempts to mitigate the negative
effects of data heterogeneity in FL by aligning each client's feature space.
Besides, we tackle the challenge of varying objective function and provide
convergence guarantee for FedFM. In FedFM, to mitigate the phenomenon of
overlapping feature spaces across categories and enhance the effectiveness of
feature matching, we further propose a more precise and effective feature
matching loss called contrastive-guiding (CG), which guides each local feature
to match with the corresponding anchor while keeping away from
non-corresponding anchors. Additionally, to achieve higher efficiency and
flexibility, we propose a FedFM variant, called FedFM-Lite, where clients
communicate with server with fewer synchronization times and communication
bandwidth costs. Through extensive experiments, we demonstrate that FedFM with
CG outperforms several works by quantitative and qualitative comparisons.
FedFM-Lite can achieve better performance than state-of-the-art methods with
five to ten times less communication costs.
