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Found 10 papers, 3 papers with code
Improving Federated Aggregation with Deep Unfolding Networks
By incorporating unbiased weights into the model, the proposed approach effectively addresses quality-aware aggregation under the heterogeneity of the participating clients and the FL environment.
Quantum Federated Learning: Analysis, Design and Implementation Challenges
Quantum Federated Learning (QFL) has gained significant attention due to quantum computing and machine learning advancements.
Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation
To this end, we develop a decentralized and trustworthy quantum federated learning (QFL) framework.
Deakin RF-Sensing: Experiments on Correlated Knowledge Distillation for Monitoring Human Postures with Radios
The proposed CKD framework transfers and fuses pose knowledge from a robust "Teacher" model to a parameterized "Student" model, which can be a promising technique for obtaining accurate yet lightweight pose estimates.
Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Framework
We design a model of Post Quantum Cryptography (PQC) Quantum Federated Learning (QFL).
Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computing
We aim to employ Federated Learning (FL) and prominent features of blockchain into MEC architecture such as connected autonomous vehicles to enable complete decentralization, immutability, and rewarding mechanisms simultaneously.
Fair and Efficient Distributed Edge Learning with Hybrid Multipath TCP
The bottleneck of distributed edge learning (DEL) over wireless has shifted from computing to communication, primarily the aggregation-averaging (Agg-Avg) process of DEL.
FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learning
These impending challenges in the design philosophy of FL call for blockchain-based federated learning (BFL) due to the benefits of coupling FL and blockchain (e. g., democracy, incentive, and immutability).
Ranked #1 on
Malicious Detection
on MNIST
Modeling RIS Empowered Outdoor-to-Indoor Communication in mmWave Cellular Networks
With the increasing adoption of millimeter-waves (mmWave) over cellular networks, outdoor-to-indoor (O2I) communication has been one of the challenging research problems due to high penetration loss of buildings.
Federated learning with multichannel ALOHA
In this paper, we study federated learning in a cellular system with a base station (BS) and a large number of users with local data sets.
Information Theory Signal Processing Information Theory
