… personalized knowledge transfer in each FL training round. To this end, we formulate the
aggregation phase in FL to a personalized group knowledge transfer … a personalized soft …

… , ∀ i, k ∈ [K] in a non-personalized FL setting. The results also present strong motivation for
clustered knowledge transfer for personalized FL. The proof for Theorem 4.2 is presented in …

… Federated learning (FL) [1] has enabled the use of data on … train machine learning models
without having to transfer the data … of knolwedge transfer well known as knowledge distillation (…

… gap, we propose the personalized Federated Knowledge Transfer (pFedKT) approach. It
involves dual knowledge transfer: (1) transferring historical local knowledge to local models via …

… settings, standard FL facilitates collaboration and knowledge sharing amongst clients but
does not entail personalized outputs as it relies on a shared global model for client inference. …

… Model personalization: the final trained … learn personalized models. For example, we can
fine-tune the client model for several epochs to see if the combination of such a personalized …

… This leads to slow convergence and degraded learning performance. As … knowledge transfer
(Def-KT) algorithm, where local clients fuse models by transferring their learned knowledge …

… However, the hypernetwork usually requires significant training efforts to achieve convergence
which may even prevent adaptive knowledge transfer among clients. Furthermore, these …

… datasets, we show the proposed method achieves significant speedups and high personalized
performance of local models. Furthermore, the proposed approach offers a more stable …

… and meanwhile enjoy the benefit from federated learning for collective knowledge sharing.
In this paper, we advocate a personalized federated learning framework for intelligent IoT …