As contemporary systems are being operated in dynamic situations alternating into decentralized and distributed environments from conventional centralized frameworks, Federated Learning (FL) has been gaining attention for an effective architecture when aggregating the information and correspondingly trained AI model of geographically distributed datasets. However, the convergence of FL suffers from the skewed and biased non-IID local dataset acquired from the heterogeneous environment, which is common in real-world practice. To address this issue, we propose a novel Label-wise clustering algorithm in FL (FedLC) that guarantees the convergence among local clients that hold unique distribution. We theoretically analyze the non-IID attributes that potentially affect the performance, defining the six non-IID scenarios in hierarchical order. Through conducting experiments on the suggested non-IID scenarios, we empirically show that the vanilla FL aggregation model is incapable of gaining stable convergence, generating a biased model. By pre-evaluating the local dataset prior to training models, FedLC determines the potential contributions with respect to trainability in a global view, and adaptively selects the locals to collaboratively take part in while aggregation. Our experimental results show that the FedLC outperforms the state-of-the-art non-IID FL optimization studies, offering a robust convergence in a highly skewed and biased non-IID dataset.

In recent years, significant strides in deep learning have propelled the advancement of electromyography (EMG)-based upper-limb gesture recognition systems, yielding notable successes across a spectrum of domains, including rehabilitation, orthopedics, robotics, and human-computer interaction. Despite these achievements, prevailing methodologies often overlook the intrinsic physical configurations and interconnectivity of multi-channel sensory inputs, resulting in a failure to adequately capture relational information embedded within the connections of deployed EMG sensor network topology. This oversight poses a significant challenge, impeding the extraction of crucial features from collaborative multi-channel EMG inputs and subsequently constraining model performance, generalizability, and interpretability. To address these limitations, we introduce novel graph structures meticulously crafted to encapsulate the spatial proximity of distributed EMG sensors and the temporal adjacency of EMG signals. Harnessing these tailored graph structures, we present Graph Convolution Network (GCN)-based classification models adept at effectively extracting and aggregating key features associated with various gestures. Our methodology exhibits remarkable efficacy, achieving state-of-the-art performance across five publicly available datasets, thus underscoring its prowess in gesture recognition tasks. Furthermore, our approach provides interpretable insights into muscular activation patterns, thereby reaffirming the practical effectiveness of our GCN model. Moreover, we show the effectiveness of our graph-based input structure and GCN-based classifier in maintaining high accuracy even with reduced sensor configurations, suggesting their potential for seamless integration into AI-powered rehabilitation strategies utilizing EMG-based gesture classification systems.

Federated learning (FL) stands as a robust framework facilitating collaborative learning in distributed Internet of Things (IoT) settings. Nevertheless, the proliferation of nonidentically and independently distributed (non-IID) data sets presents formidable challenges, revealing distinct attributes among local participants within the IoT network. In response, clustering has been incorporated into FL methodologies, bolstering adaptability to heterogeneous data set characteristics and ameliorating the effects of non-IID conditions. Despite its effectiveness, the interpretive aspects and ramifications of clustering within FL have received insufficient scrutiny, constraining its broader utility in artificial intelligence-infused IoT ecosystems. This study exposes a notable drawback in traditional clustering methods within FL, labeled as “Cluster Collapse Disorder,” wherein cluster boundaries blur, leaving models susceptible to non-IID data and data drift. Inspired by this finding, we propose clustering-based semantic FL (CLSM-FL). Our framework employs a soft clustering technique based on the inherent attributes of local models, assigning them to semantic clusters for interpretable evaluation of their capabilities. Beyond its interpretive benefits, CLSM-FL ensures resilient performance in non-IID scenarios and against data-perturbed adversarial attacks. We substantiate its efficacy through extensive experiments covering various scenarios and data sets. This contribution not only underscores the crucial role of clustering in FL but also advances comprehension and practical application of FL methodologies in non-IID IoT environments.

Objective. Upper limb loss can profoundly impact an individual’s quality of life, posing challenges to both physical capabilities and emotional well-being. To restore limb function by decoding electromyography (EMG) signals, in this paper, we present a novel deep prototype learning method for accurate and generalizable EMG-based gesture classification. Existing methods suffer from limitations in generalization across subjects due to the diverse nature of individual muscle responses, impeding seamless applicability in broader populations. Approach. By leveraging deep prototype learning, we introduce a method that goes beyond direct output prediction. Instead, it matches new EMG inputs to a set of learned prototypes and predicts the corresponding labels. Main results. This novel methodology significantly enhances the model’s classification performance and generalizability by discriminating subtle differences between gestures, making it more reliable and precise in real-world applications. Our experiments on four Ninapro datasets suggest that our deep prototype learning classifier outperforms state-of-the-art methods in terms of intra-subject and inter-subject classification accuracy in gesture prediction. Significance. The results from our experiments validate the effectiveness of the proposed method and pave the way for future advancements in the field of EMG gesture classification for upper limb prosthetics.

Federated Learning (FL)-based modulation decoding enables collaborative learning across distributed Artificial Intelligence (AI)-powered modulational signal decoding models in large-scale wireless communication networks. Despite significant advancements, FL-based modulation decoding faces two primary challenges: (1) Vulnerability to noisy environments - Current studies often prioritize high-quality datasets during training to ensure model performance, which often results in overfitting since real-world signals involve a wide range of noise levels from various sources. (2) Model divergence in non-Independent and Identically Distributed (non-IID) environments - The aggregation step in a conventional FL leads to divergence on a large federated scale, resulting in the loss of local-specific knowledge. Moreover, existing FL-based modulation decoding studies typically focus on a single non-IID issue, which limits their ability to generalize across large-scale heterogeneous settings. To address these challenges, we propose FedVaccine, a framework that enhances model generalization across diverse noise conditions and non-IID data environments. Instead of relying on high-quality datasets that risk overfitting, FedVaccine employs a harmonic noise resilience approach—akin to a vaccination effect—that deliberately exposes the model to controlled noise, identifying noise tolerance, regularizing training, and ensuring robust decoding performance across varying noise levels. Additionally, FedVaccine incorporates a Federated Split Learning approach to cumulatively update the global model, ensuring adaptive recalibration and mitigating the loss of local knowledge during aggregation. Furthermore, a localized support set is used to ensure effective personalization. Experimental results demonstrate that FedVaccine outperforms existing FL-based modulation classification models and other learning paradigms in both IID and non-IID scenarios using public datasets, showcasing its effectiveness in practical deployment scenarios across wireless networks.

COVID-19 vaccine distribution route directly impacts the community's mortality and infection rate. Therefore, optimal vaccination dissemination would appreciably lower the death and infection rates. This paper proposes the Epidemic Vulnerability Index (EVI) that quantitatively evaluates the subject's potential risk. Our primary aim for the suggested index is to diminish both infection rate and death rate efficiently. EVI was accordingly designed with clinical factors determining the mortality and social factors incorporating the infection rate. Through statistical COVID-19 patient dataset analysis and social network analysis with an agent-based model that is analogous to a real-world system, we define and experimentally validate the capability of EVI. Our experiments consist of nine vaccination distribution scenarios, including existing indexes which estimate the risk and stochastically proliferate the contagion and vaccine in a 300,000 agent-based graph network. We compared the outcome and variation of the three metrics in the experiments: infection case, death case, and death rate. Through this assessment, vaccination by the descending order of EVI has shown to have a significant outcome with an average of 5.0% lower infection cases, 9.4% lower death cases, and 3.5% lower death rate than other vaccine distribution routes.