Search Results for author:
Found 6 papers, 2 papers with code
Differentially-Private Hierarchical Federated Learning
While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from the communicated model parameters.
Delay-Aware Hierarchical Federated Learning
The paper introduces delay-aware hierarchical federated learning (DFL) to improve the efficiency of distributed machine learning (ML) model training by accounting for communication delays between edge and cloud.
Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity
Federated learning (FL) has emerged as a popular technique for distributing machine learning across wireless edge devices.
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling
Federated learning has emerged as a popular technique for distributing model training across the network edge.
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations
Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge.
Federated Learning with Communication Delay in Edge Networks
Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks.
