federated learning

Federated privacy-preserving facial paralysis severity classification using hybrid deep learning and pufferfish optimization

Abstract Facial paralysis (FP) severity assessment requires accurate identification of subtle facial asymmetry while maintaining the confidentiality of sensitive patient information. Conventional centralized deep learning approaches require patient images to be transferred to a central repository, which can increase pr...

Privacy-Preserving Continual Learning for Detecting Concept Drift in Distributed Artificial Intelligence Systems

P2PFL: A modular framework for Decentralized Federated Learning

Decentralized Federated Learning (DFL) offers a promising paradigm for collaborative machine learning training without centralized coordination, addressing critical limitations of centralized federated learning such as single points of failure, communication bottlenecks, and scalability constraints. However, the adopti...

Introduction: The Convergence of AI Wearables and the Metaverse

The ongoing advancements in artificial intelligence (AI) and wearable sensors coupled with the metaverse touch every field of study, as they intertwine the physical, digital, and cognitive realms in an unprecedented way. In this work, we establish an initial AI, wearables, and metaverse interconnection point in the fie...

Blockchain-Anchored Reputation for Robust Aggregation in Homomorphically Encrypted Federated Learning — code and data

Blockchain-Anchored Reputation for Robust Aggregation in Homomorphically Encrypted Federated LearningCode, data splits, results and figures.

Cross-Cancer Expert-Routing Knowledge Transfer in Federated Prognosis Prediction

Lightweight And Privacy-Preserving Intrusion Detection For The Internet Of Things: A Systematic Review Of Datasets, Models, And Emerging Directions

The rapid expansion of Internet of Things (IoT) deployments has been matched by a corresponding rise in device- and network-targeted attacks, while the limited computation, memory, and energy budgets of most IoT endpoints make conventional, resource-intensive intrusion detection systems (IDS) difficult to deploy direct...

FedREST-HIDS: split-federated intrusion detection for resource-heterogeneous IIoT

FedREST-HIDS Notebooks and results accompanying "FedREST-HIDS: Split-Federated Intrusion Detection for Resource-Heterogeneous Industrial IoT" (Vyas, Hwang & Lin), submitted to Computer Communications. FedREST-HIDS partitions intrusion detection across three tiers. Resource-rich (RR) devices train complete local models...

Mitigating client heterogeneity in federated learning: a reinforcement learning approach for efficient client selection

Federated learning (FL) is a highly promising distributed machine learning method that enables collaborative model training while protecting participants’ data. In Internet of Things (IoT) applications, due to the large number of devices and high communication overhead, selecting a subset of clients for each training r...

A comprehensive survey of machine and deep learning as well as explainable artificial intelligence approaches for heart disease detection

Cardiovascular diseases (CVDs) remain the foremost cause of mortality worldwide, claiming approximately 17.9 million lives annually and representing 32% of all global deaths. Timely and accurate detection of heart disease is therefore of paramount importance. This paper presents a narrative survey, synthesising 57 peer...

Federated hybrid quantum convolutional neural network for Alzheimer’s disease classification using magnetic resonance imaging

Alzheimer’s disease (AD) is a progressive neurological disorder and one of the most common types of dementia, causing impaired memory and quality of life. Therefore, an early and accurate diagnosis is essential for intervention and patient care. Convolutional Neural Networks (CNNs) and other deep learning models have d...

Blockchain-Anchored Reputation for Robust Aggregation in Homomorphically Encrypted Federated Learning — code and data

Blockchain-Anchored Reputation for Robust Aggregation in Homomorphically Encrypted Federated LearningCode, data splits, results and figures.