[HTML][HTML] Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability
Smart grid is a transformative advancement that modernized the traditional power system for
effective electricity management, and involves optimized energy distribution by load …
effective electricity management, and involves optimized energy distribution by load …
SAM: An Efficient Approach With Selective Aggregation of Models in Federated Learning
Federated learning (FL) is a promising distributed learning mechanism that revolutionizes
our interaction with data in the IoT ecosystem. Due to the rapidly growing scale of smart …
our interaction with data in the IoT ecosystem. Due to the rapidly growing scale of smart …
Automatic Layer Freezing for Communication Efficiency in Cross-Device Federated Learning
Federated learning (FL) is a collaborative machine learning paradigm where network-edge
clients train a global model under the orchestration of a central server. Unlike traditional …
clients train a global model under the orchestration of a central server. Unlike traditional …
Hierarchical federated learning in wireless networks: Pruning tackles bandwidth scarcity and system heterogeneity
While a practical wireless network has many tiers where end users do not directly
communicate with the central server, the users' devices have limited computation and …
communicate with the central server, the users' devices have limited computation and …
Towards efficient federated learning: Layer-wise pruning-quantization scheme and coding design
As a promising distributed learning paradigm, federated learning (FL) faces the challenge of
communication–computation bottlenecks in practical deployments. In this work, we mainly …
communication–computation bottlenecks in practical deployments. In this work, we mainly …
ISFL: Federated Learning for Non-iid Data with Local Importance Sampling
As a promising learning paradigm integrating computation and communication, federated
learning (FL) proceeds the local training and the periodic sharing from distributed clients …
learning (FL) proceeds the local training and the periodic sharing from distributed clients …
Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey
W Yang, S Wang, D Wu, T Cai, Y Zhu, S Wei… - arxiv preprint arxiv …, 2025 - arxiv.org
The rapid adoption of deep learning in sensitive domains has brought tremendous benefits.
However, this widespread adoption has also given rise to serious vulnerabilities, particularly …
However, this widespread adoption has also given rise to serious vulnerabilities, particularly …
Joint Layer Selection and Differential Privacy Design for Federated Learning Over Wireless Networks
In this work, the problem of training the secure federated learning (FL) algorithm over a
multicell wireless network is investigated. FL is indeed a learning method that can protect …
multicell wireless network is investigated. FL is indeed a learning method that can protect …
Amplitude-aligned personalization and robust aggregation for federated learning
Y Jiang, S Chen, X Bao - IEEE Transactions on Sustainable …, 2023 - ieeexplore.ieee.org
In practical applications, federated learning (FL) suffers from slow convergence rate and
inferior performance resulting from the statistical heterogeneity of distributed data …
inferior performance resulting from the statistical heterogeneity of distributed data …
Toward Efficient and Secure Object Detection With Sparse Federated Training Over Internet of Vehicles
Internet of Vehicles (IoV) plays a vital role in alleviating traffic issues. Object detection is one
of the key technologies in IoV, which has been widely used to provide traffic management …
of the key technologies in IoV, which has been widely used to provide traffic management …