When federated learning meets watermarking: A comprehensive overview of techniques for intellectual property protection
Federated learning (FL) is a technique that allows multiple participants to collaboratively
train a Deep Neural Network (DNN) without the need to centralize their data. Among other …
train a Deep Neural Network (DNN) without the need to centralize their data. Among other …
Differentially private federated learning: A systematic review
In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …
federated learning to the forefront of research. Differential privacy has emerged as the de …
FedDP-SA: Boosting Differentially Private Federated Learning via Local Dataset Splitting
Federated learning (FL) emerges as an attractive collaborative machine learning framework
that enables training of models across decentralized devices by merely exposing model …
that enables training of models across decentralized devices by merely exposing model …
[HTML][HTML] Balancing privacy and performance in federated learning: A systematic literature review on methods and metrics
Federated learning (FL) as a novel paradigm in Artificial Intelligence (AI), ensures enhanced
privacy by eliminating data centralization and brings learning directly to the edge of the …
privacy by eliminating data centralization and brings learning directly to the edge of the …
Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPS
Abstract In Cyber-Physical Systems (CPS), distributed learning is essential for efficiently
handling complex tasks when sufficient resources are available. However, when resources …
handling complex tasks when sufficient resources are available. However, when resources …
Differential Privacy-Aware Generative Adversarial Network-Assisted Resource Scheduling for Green Multi-Mode Power IoT
S Zhang, J Xue, J Liu, Z Zhou, X Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The low-carbon and efficient operation of smart parks requires high-precision and real-time
energy management model training. Multi-mode power internet of things (PIoT) consisting of …
energy management model training. Multi-mode power internet of things (PIoT) consisting of …
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Federated Learning (FL) offers innovative solutions for privacy-preserving collaborative
machine learning (ML). Despite its promising potential, FL is vulnerable to various attacks …
machine learning (ML). Despite its promising potential, FL is vulnerable to various attacks …
FeaShare: Feature Sharing for Computation Correctness in Edge Preprocessing
Z Zhao, H Bin, H Li, N Yu, H Zhu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Edge preprocessing is a critical service type in edge computing. However, untrusted edges
may be malicious to provide incorrect computational results (ie, edge tampering). Although …
may be malicious to provide incorrect computational results (ie, edge tampering). Although …
TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance
As a key technology in 6G research, federated learning (FL) enables collaborative learning
among multiple clients while ensuring individual data privacy. However, malicious attackers …
among multiple clients while ensuring individual data privacy. However, malicious attackers …
Privacy-Preserving Federated Class-Incremental Learning
J Xiao, XM Tang, SF Lu - IEEE Transactions on Machine …, 2023 - ieeexplore.ieee.org
Federated Learning (FL) offers a collaborative training framework, aggregating model
parameters from decentralized clients. Many existing models, however, assume static …
parameters from decentralized clients. Many existing models, however, assume static …