Asynchronous proximal federated aggregation for heterogeneous healthcare networks

A Feature-Augmented Analytic Federated Architecture for Early Sepsis Detection

The proliferation of Internet of Medical Things devices within the predictive healthcare paradigm necessitates robust, privacy-centric collaborative learning frameworks to detect and mitigate rapid clinical deterioration. Traditional federated learning methodologies, while attempting to preserve patient data locality, are fundamentally constrained by multi-round gradient synchronization protocols, imposing prohibitive communication latency and remaining susceptible to false negatives under extreme non-independent and identically distributed conditions. To address these challenges, this study introduces the Feature-Augmented Analytic Federated (FaFL) Architecture, which fundamentally replaces iterative gradient synchronization with a single-round closed-form computational paradigm. By instituting a proactive feature mixing mechanism via a decoupled zero-knowledge proof global buffer, the proposed framework empowers local grassroots nodes to neutralize extreme clinical heterogeneity in a single phase. The architecture employs a closed-form analytic solution combined with a trace-weighted absolute aggregation protocol to rigorously guarantee stochastic convergence and absolute cryptographic resilience without requiring recursive parameter exchanges. Extensive empirical evaluations against existing baselines under severe Dirichlet non-independent and identically distributed conditions and Byzantine poisoning attacks demonstrate that the framework fundamentally eradicates high false-negative rates in resource-constrained clinics. Consequently, the proposed architecture robustly guarantees generalization stability, substantially outperforms existing paradigms in predictive fidelity and computational efficiency, and establishes a new operational standard for mission-critical clinical networks.

Dynamic Trust-Aware Federated Aggregation for Privacy-Aware Multicentric Healthcare AI

FedQS: asynchronous federated learning based on queue scheduling

With the rapid development of the Internet of Things (IoT) and edge computing, Federated Learning (FL) has emerged as a promising distributed framework capable of effectively leveraging distributed devices for machine learning tasks while preserving data privacy. However, in practical scenarios characterized by significant device heterogeneity and communication delays, traditional synchronous FL often suffers from inefficiency due to the straggler effect. Although Asynchronous Federated Learning (AFL) has been proposed to mitigate this, it still faces challenges regarding model staleness and training bias, which hinder convergence performance. To address these issues, this paper presents an asynchronous framework named FedQS. First, FedQS employs a multi-dimensional staleness evaluation mechanism that dynamically assesses updates by combining the similarity between local and global models with client latency metrics. Second, to resolve the coupling between training bias and model staleness, we implement a decoupling solution via a queue scheduling algorithm. This algorithm reprocesses high-staleness model parameters on selected faster helper clients using only the helpers’ local private data while preserving the provenance of the original uploading clients, thereby aiming to reduce stale-update effects in aggregation. Finally, during the aggregation phase, the framework recalibrates aggregation weights based on client contributions to reduce training bias and improve global model accuracy. Comparative experiments on Fashion-MNIST and CIFAR-10 datasets demonstrate that FedQS outperforms four evaluated representative baselines—FedAsync, FedBuff, KAFL, and CAFL—achieving an average accuracy improvement of 8.62%.

Hierarchical federated edge learning with criticality-aware aggregation for privacy-preserving fetal health classification

The automated classification of fetal health from cardiotocographic (CTG) recordings is a crucial machine learning application in obstetric diagnostics. Although centralized predictive models have advanced significantly, their large-scale clinical deployment remains structurally constrained by institutional data privacy regulations, data silos, and significant distributional heterogeneity across different recording equipment. To address these interconnected challenges, this study introduces a novel Criticality-Aware Federated Learning framework with Hierarchical Edge Aggregation, designated as FedCrit-HEA. The primary architectural novelty of this framework lies in its two-tier hierarchical federated topology combined with an adaptive optimization layer. Hospital clients optimize local parameters independently and transmit differentially private gradients to regional edge servers for preliminary aggregation, which systematically isolates wide-area network bottlenecks. Crucially, the framework introduces a novel criticality-aware global aggregation mechanism that dynamically adjusts client parameter contributions based on a pathological-sample density metric derived from localized class-distribution statistics. This targeted operator inherently prevents the suppression of clinically critical minority-class gradient information by normal-class dominant clients, which is a persistent limitation in standard federated averaging. Validated across two distinct clinical repositories—the UCI Cardiotocography and the CTU-CHB Dataset—the framework achieves a global macro-averaged F1-score of 0.904 and a pathological-class recall of 84.2% on the UCI partition, alongside an F1-score of 0.901 and a pathological recall of 85.6% on the CTU-CHB data. Simultaneously, the hierarchical edge-aggregation layout reduces wide-area network communication overhead by up to 75% compared to conventional flat federated configurations. These outcomes demonstrate the feasibility and architectural robust novelty of collaborative, privacy-preserving, and minority-sensitive diagnostic modeling within distributed healthcare ecosystems.

Efficient Federated LLM Framework for Intelligent IoMT Management

Straggler-Aware Asynchronous Federated Learning via Temporal Cost Clustering for Fair and Efficient Heterogeneous Model Training

Heterogeneous federated learning leads to system and data differentials that cause stragglers to either be a bottleneck to synchronous optimization or create representation bias in asynchronous contexts. Although current approaches deal with staleness or buffering independently, their approach does not ensure fast clients do not take over the global model. The proposed framework Straggler-Aware Asynchronous Federated Learning (SAFL), that re-defines the stragglers as structured subjects rather than outliers. SAFL employs temporal exponentially weighted moving average signature of client costs and costs model updates by clustering costs in time-constrained per-cluster buffers. An innovative fairness-sensitive aggregation scheme then balances the participation through frequency compensation and damping on staleness. The results of the experiment indicate that SAFL achieves a 75% accuracy in 620 seconds, 27% higher than the state-of-the-art Federated Asynchronous Mobile Update (FedASMU) and increases the fairness index by 0.52 to 0.87. SAFL has a scalable, fair approach to the regulation of heterogeneous clusters, which means they can be used to ensure almost equal contribution in regulated settings such as financial and healthcare analytics.