Blockchain-based federated learning for securing internet of things: A comprehensive survey
The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …
significant advantages in agility, responsiveness, and potential environmental benefits. The …
Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
Feddc: Federated learning with non-iid data via local drift decoupling and correction
Federated learning (FL) allows multiple clients to collectively train a high-performance
global model without sharing their private data. However, the key challenge in federated …
global model without sharing their private data. However, the key challenge in federated …
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 …
A state-of-the-art survey on solving non-iid data in federated learning
Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …
researchers in that it can enable multiple clients to cooperatively train global models without …
Towards out-of-distribution generalization: A survey
Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …
test data follow the same statistical pattern, which is mathematically referred to as …
A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions
The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …
However, new privacy concerns have also emerged during the aggregation of the …
Data-free knowledge distillation for heterogeneous federated learning
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …
server iteratively averages the model parameters of local users without accessing their data …
Model-contrastive federated learning
Federated learning enables multiple parties to collaboratively train a machine learning
model without communicating their local data. A key challenge in federated learning is to …
model without communicating their local data. A key challenge in federated learning is to …