An adaptive FL framework that employs a hybrid contribution evaluation mechanism as the core principle for client selection and resource management is proposed and demonstrates that the proposed framework outperforms existing FL baselines in terms of training efficiency, data utilization, and fairness.
Abstract
Federated Learning (FL) enables collaborative model training across decentralized data silos while preserving data privacy. However, client selection strategies in conventional FL processes typically rely on single-dimensional evaluation metrics, which fail to capture data diversity and overlook the dynamic nature of client contributions, particularly in domains characterized by sparse and heterogeneous data, such as healthcare and drug discovery. These limitations ultimately hinder the global model's generalization ability and reduce training efficiency. To address these challenges, this paper proposes an adaptive FL framework that employs a hybrid contribution evaluation mechanism as the core principle for client selection and resource management. The proposed approach quantifies each client's effectiveness by integrating two complementary dimensions: (i) a performance-based evaluation that measures the immediate impact of a client's update on the global optimization trajectory, and (ii) a coverage-based evaluation that estimates data diversity in the latent embedding space without exposing raw data. By combining these two criteria, the hybrid mechanism ensures that highly contributive clients are preferentially selected while preventing the permanent exclusion of any participant, thereby maintaining a balanced trade-off between efficiency and fairness. Experimental results demonstrate that the proposed framework outperforms existing FL baselines in terms of training efficiency, data utilization, and fairness.
SynPre-FL is proposed, a unified framework combining high-fidelity synthetic EHR generation with synthetic-pretrained FL for robust prediction under non-IID conditions and provides a practical and reproducible framework to enable privacy-aware, interpretable, and robust clinical prediction from distributed tabular EHR data.
Akarsh K. Nair, Muhammad Arifur Rahman, N. Shopland et al.· 0 citations
A PFL framework, FedSCF, which models client heterogeneity at the parameter level, including a relative perturbation-based sensitivity evaluation is designed to identify critical parameters for personalized modeling, while the remaining parameters participate in cross-client sharing.
Mingjun Wei, Rongyang Xu, Qian Zhang et al.· Engineering Research Express· 0 citations
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 personalized aggregation set for each client.
Mirko Konstantin, S. Zachow, Anirban Mukhopadhyay· 0 citations
Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously. This dissertation presents methodologies for building efficient personalized models by identifying which strategies are effective in the global training stage and by showing how to preserve global knowledge while securing user-specific performance during local adaptation. First, we show that as data heterogeneity increases, the collapse of feature vectors is a more fundamental bottleneck than classifier weights, and propose a method that directly mitigates the discrepancy in representation magnitude between local and global models. Second, we analyze that a training approach that strengthens local alignment can induce forgetting of global knowledge (e.g., categories not observed locally), and propose a method that achieves both local alignment and global knowledge preservation by combining feature distillation based on the global model's feature vectors. Third, in federated personalized reward model learning with preference heterogeneity, we empirically verify the conventional belief that"increasing the number of global models yields better initialization,"and we show that when sufficient local fine-tuning is allowed, a single global initialization can instead provide stronger personalization performance. This study redefines the role of global initialization under data and preference heterogeneity and provides practical training strategies that simultaneously satisfy global knowledge preservation and personalization.
A new FAL framework is proposed that utilizes federated representation learning to align client data in a shared embedding space that achieves performance that surpasses existing FAL methods even when they are given substantially larger annotation budgets, demonstrating the value of centralized coordination under privacy constraints.
The proposed approach displays improvement in accuracy and uniformity across client accuracy by enhancing the fairness of the federated learning system using a novel distribution-aware algorithm.
Sushant Jain, Sumukh Gupta, Amit Pundir et al.· Neural computing & applicati...· 0 citations