A network resource aware federated learning approach using knowledge distillation
Federated Learning (FL) has gained unprecedented growth in the past few years by
facilitating data privacy. This poster proposes a network resource aware federated learning …
facilitating data privacy. This poster proposes a network resource aware federated learning …
FedRDS: federated learning on non-iid data via regularization and data sharing
Federated learning (FL) is an emerging decentralized machine learning framework enabling
private global model training by collaboratively leveraging local client data without …
private global model training by collaboratively leveraging local client data without …
Federated Learning on Non-iid Data via Local and Global Distillation
Most existing federated learning algorithms are based on the vanilla FedAvg scheme.
However, with the increase of data complexity and the number of model parameters, the …
However, with the increase of data complexity and the number of model parameters, the …
Exploiting features and logits in heterogeneous federated learning
Due to the rapid growth of IoT and artificial intelligence, deploying neural networks on IoT
devices is becoming increasingly crucial for edge intelligence. Federated learning (FL) …
devices is becoming increasingly crucial for edge intelligence. Federated learning (FL) …
Quality-aware incentive mechanism design based on matching game for hierarchical federated learning
D Hui, L Zhuo, C Xin - IEEE INFOCOM 2022-IEEE Conference …, 2022 - ieeexplore.ieee.org
To protect user privacy and combined with mobile edge computing, hierarchical federated
learning (HFL) is proposed. In HFL, we investigated the aggregated model quality …
learning (HFL) is proposed. In HFL, we investigated the aggregated model quality …
Fine-tuning global model via data-free knowledge distillation for non-iid federated learning
Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …
FLIGHT: Federated learning with IRS for grouped heterogeneous training
In recent years, federated learning (FL) has played an important role in private data-
sensitive scenarios to perform learning tasks collectively without data exchange. However …
sensitive scenarios to perform learning tasks collectively without data exchange. However …
An efficient multi-model training algorithm for federated learning
How to effectively organize various heterogeneous clients for effective model training has
been a critical issue in federated learning. Existing algorithms in this aspect are all for single …
been a critical issue in federated learning. Existing algorithms in this aspect are all for single …
TKAGFL: a federated communication framework under data heterogeneity
Federated learning still faces many problems from research to technology implementation
and the most critical problem is that the communication efficiency is not high. Therefore, the …
and the most critical problem is that the communication efficiency is not high. Therefore, the …
FedTCR: communication-efficient federated learning via taming computing resources
Federated learning (FL) enables clients learning a shared global model from multiple
distributed devices while keeping training data locally. Due to the synchronous update mode …
distributed devices while keeping training data locally. Due to the synchronous update mode …