Revealing the landscape of privacy-enhancing technologies in the context of data markets for the IoT: A systematic literature review
IoT data markets in public and private institutions have become increasingly relevant in
recent years because of their potential to improve data availability and unlock new business …
recent years because of their potential to improve data availability and unlock new business …
[HTML][HTML] Over-the-air federated learning: Status quo, open challenges, and future directions
The development of applications based on artificial intelligence and implemented over
wireless networks is increasingly rapidly and is expected to grow dramatically in the future …
wireless networks is increasingly rapidly and is expected to grow dramatically in the future …
Privacy-preserving aggregation in federated learning: A survey
Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …
Fairness and privacy preserving in federated learning: A survey
Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …
addresses privacy concerns by allowing participants to collaboratively train machine …
LaF: Lattice-based and communication-efficient federated learning
Federated learning is an emerging technology which allows a server to train a global model
with the cooperation of participants without exposing the participants' data. In recent years …
with the cooperation of participants without exposing the participants' data. In recent years …
A comprehensive survey on privacy-preserving techniques in federated recommendation systems
Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …
address various challenges. One such development is the use of federated learning for …
Hwamei: A learning-based synchronization scheme for hierarchical federated learning
Federated learning (FL) enables collaborative model training among distributed devices
without data sharing, but existing FL suffers from poor scalability because of global model …
without data sharing, but existing FL suffers from poor scalability because of global model …
FLIBD: A federated learning-based IoT big data management approach for privacy-preserving over Apache Spark with FATE
A Karras, A Giannaros, L Theodorakopoulos… - Electronics, 2023 - mdpi.com
In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big
Data, intricately designed to ensure privacy preservation across extensive system networks …
Data, intricately designed to ensure privacy preservation across extensive system networks …
Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo
federated learning (cross-silo FL), wherein organizations, ie, clients, collaboratively train …
federated learning (cross-silo FL), wherein organizations, ie, clients, collaboratively train …
A survey on class imbalance in federated learning
J Zhang, C Li, J Qi, J He - arXiv preprint arXiv:2303.11673, 2023 - arxiv.org
Federated learning, which allows multiple client devices in a network to jointly train a
machine learning model without direct exposure of clients' data, is an emerging distributed …
machine learning model without direct exposure of clients' data, is an emerging distributed …