  In a practical setting, how to enable robust Federated Learning (FL) systems,
both in terms of generalization and personalization abilities, is one important
research question. It is a challenging issue due to the consequences of
non-i.i.d. properties of client's data, often referred to as statistical
heterogeneity, and small local data samples from the various data
distributions. Therefore, to develop robust generalized global and personalized
models, conventional FL methods need to redesign the knowledge aggregation from
biased local models while considering huge divergence of learning parameters
due to skewed client data. In this work, we demonstrate that the knowledge
transfer mechanism achieves these objectives and develop a novel knowledge
distillation-based approach to study the extent of knowledge transfer between
the global model and local models. Henceforth, our method considers the
suitability of transferring the outcome distribution and (or) the embedding
vector of representation from trained models during cross-device knowledge
transfer using a small proxy dataset in heterogeneous FL. In doing so, we
alternatively perform cross-device knowledge transfer following general
formulations as 1) global knowledge transfer and 2) on-device knowledge
transfer. Through simulations on three federated datasets, we show the proposed
method achieves significant speedups and high personalized performance of local
models. Furthermore, the proposed approach offers a more stable algorithm than
other baselines during the training, with minimal communication data load when
exchanging the trained model's outcomes and representation.
