This paper proposes a reputation-based federated learning framework to mitigate potential security threats in EEG signal classification. The framework is useful in the growing field of brain-computer interface technology, where…
Continue readingThis paper proposes a new Privacy-Preserving TFGC (Target Fine-grained Classification) Framework based on Federated Learning, named PRFL. The framework is designed to help clients learn global and local knowledge, improving…
Continue readingThe article introduces ProvFL, a mechanism that aids in interpreting and explaining global model predictions in Federated Learning (FL). It’s designed to track the information flow between individual clients in…
Continue readingThe paper presents a novel algorithm, Federated Learning with Accumulated Regularized Embeddings (FLARE), which addresses challenges in decentralized machine learning, specifically in federated learning (FL). The algorithm uses sparse training…
Continue readingThis research paper introduces Fed-CO2, a universal Federated Learning (FL) framework designed to handle both label distribution skew and feature skew. It uses a cooperation mechanism between online and offline…
Continue readingThe article discusses the use of blockchain technology to address data privacy and security issues in collaborative machine learning over Internet of Things (IoT) devices. Traditional centralized machine learning models…
Continue readingThe paper discusses the vulnerability of Federated Learning (FL) protocols to active reconstruction attacks by dishonest servers. These attacks involve the malicious modification of global model parameters, allowing the server…
Continue readingThe research proposes a federated learning framework for intrusion detection in Internet of Vehicles (IOVs) using the CIC-IDS 2017 dataset. The framework uses SMOTE for handling class imbalance, outlier detection…
Continue readingThe article presents FedPEAT, a system that merges federated learning, parameter-efficient fine-tuning, and emulator-assisted tuning for AI foundation models with mobile edge computing. The system is designed to address challenges…
Continue readingThe article explores the use of federated learning in electricity load forecasting. The authors propose a novel transformer-based deep learning approach that improves data privacy by training models locally on…
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