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Conference Jul 2026

Improving Federated Learning under Non-IID Data through Clustered Ensembles and Adaptive Client Selection

Federated Learning (FL) enables collaborative model training without sharing raw data, but its effectiveness degrades under Non-IID client data and inefficient participation. In such settings, FedAvg may exhibit slow convergence, high variance across rounds, and elevated communication overhead. We propose a clustered e...

Artur Sousa Freitas, A. T. Akabane, J. Estrella · 0 citations
2026

Asymmetric Partial Model Transmission for Federated Edge Learning

Federated learning (FL) applications normally employ large deep learning (DL) models, resulting in excessive communication overhead in the deployment of FL over resource-constraint mobile edge networks. To achieve better scalability for DL-based FL, we capitalize on both the asymmetric nature of mobile networks and the...

Zi-Han Chen, H. Yang, Tony Q. S. Quek et al. · 0 citations
Preprint Aug 2026

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in function space using an NTK-based agreement score to characterize predictive behavior and determine a persona...

Mirko Konstantin, S. Zachow, Anirban Mukhopadhyay · 0 citations
#machine learning Preprint Aug 2026

Efficient Resource Optimization for Split Federated Learning

This work establishes an efficient optimization framework for SFL under resource-constrained networks that jointly optimizes model splitting and resource allocation to minimize training cost, which is defined as the weighted sum of latency and energy costs.

Wei Wei, Xian-Hao Chen · 0 citations
2026

Toward Efficient Semi-Asynchronous Federated Learning: A Multi-Factor Grouping and Dual-Level Selection Scheme Under Heterogeneous Environments

Semi-Asynchronous Federated Learning (SAFL) takes advantage of both synchronous and asynchronous FLs to train models. However, existing works in semi-asynchronous FLs fail to fully account for heterogeneities in both data and devices. To address these issues, we first propose a Clustered SAFL (CSAFL) framework and theo...

Gang Li, Yuhang Chen, Jun Cai et al. · 0 citations

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