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Found 8 papers, 0 papers with code
Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approach
Clustered Federated Multitask Learning (CFL) has gained considerable attention as an effective strategy for overcoming statistical challenges, particularly when dealing with non independent and identically distributed (non IID) data across multiple users.
The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey
{ This is followed by a critical discussion on their practical implications and broader impact on cybersecurity in Smart Grids.}
Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks
Extensive experiments show that the proposed approach lowers the training time and accelerates the convergence rate by up to 50% while imbuing each client with a specialized model that is fit for its local data distribution.
Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey
Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements.
Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning
Specifically, we consider a problem that aims to find the optimal user's resources, including the fine-grained selection of relevant training samples, bandwidth, transmission power, beamforming weights, and processing speed with the goal of minimizing the total energy consumption given a deadline constraint on the communication rounds of FEEL.
Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning
Then, the problem is formulated as joint energy minimization and resource allocation optimization problem to obtain the optimal local computation time and the optimal transmission time that minimize the total energy consumption considering the worker's energy budget, available bandwidth, channel states, beamforming, and local CPU speed.
Exploiting Unlabeled Data in Smart Cities using Federated Learning
The algorithm is divided into two phases where the first phase trains a global model based on the labeled data.
Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method
Although crowdsourcing is an excellent way to gather immense amounts of data, it jeopardizes the privacy of participants, as it requires to collect labeled data at a centralized server.
