… federated learning (FL) is a collaborative machine learning framework that enables devices
to learn a … learning performance for vertical FL. To address these issues, we characterize the …

… federated averaging algorithm and analyze its convergence … the extension of our convergence
analysis to the case where … clarity, we do not include analysis with device sampling. Yet, …

… Although the current analysis only holds for the proposed client selection algorithm applied
… to other federated learning methods as well in the future. We note that this FL analysis is new…

… theoretical analysis framework to prove the convergence by … Weighted Clustered Federated
Learning (WeCFL). Empirical … analysis framework to conduct convergence analysis on FL …

… In Section III, we detail the proposed NbAFL and analyze the privacy performance based on
DP. In Section IV, we analyze the convergence bound of NbAFL and reveal the relationship …

… -federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS
and devices for a hybrid implementation of centralized learning (… convergence analysis by …

… federated learning is scarce and existing works either lack a complete theoretical convergence
… More importantly, we provide a convergence analysis for our method and prove that our …

… This paper presents a new class of convergence analysis for FL, Federated Learning …
trained by gradient descent in FL and is inspired by the analysis in Neural Tangent Kernel (NTK). …

… Federated Learning Let us consider a general FL … analyze the convergence bound of
the CDP in FL, which reveals an explicit tradeoff between the privacy level and convergence …

… convergence analysis technique that characterizes the distributed learning paradigm of
federated learning (FL) … The analysis developed in this paper demonstrates, for the first time, that …