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 …
Privacy and fairness in Federated learning: on the perspective of Tradeoff
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
A central challenge in training classification models in the real-world federated system is
learning with non-IID data. To cope with this, most of the existing works involve enforcing …
learning with non-IID data. To cope with this, most of the existing works involve enforcing …
Virtual homogeneity learning: Defending against data heterogeneity in federated learning
In federated learning (FL), model performance typically suffers from client drift induced by
data heterogeneity, and mainstream works focus on correcting client drift. We propose a …
data heterogeneity, and mainstream works focus on correcting client drift. We propose a …
Towards building the federatedGPT: Federated instruction tuning
While" instruction-tuned" generative large language models (LLMs) have demonstrated an
impressive ability to generalize to new tasks, the training phases heavily rely on large …
impressive ability to generalize to new tasks, the training phases heavily rely on large …
FedFed: Feature distillation against data heterogeneity in federated learning
Federated learning (FL) typically faces data heterogeneity, ie, distribution shifting among
clients. Sharing clients' information has shown great potentiality in mitigating data …
clients. Sharing clients' information has shown great potentiality in mitigating data …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
collaboration among different parties. Recently, with the popularity of federated learning, an …
Dres-fl: Dropout-resilient secure federated learning for non-iid clients via secret data sharing
Federated learning (FL) strives to enable collaborative training of machine learning models
without centrally collecting clients' private data. Different from centralized training, the local …
without centrally collecting clients' private data. Different from centralized training, the local …
Architecture agnostic federated learning for neural networks
With growing concerns regarding data privacy and rapid increase in data volume, Federated
Learning (FL) has become an important learning paradigm. However, jointly learning a deep …
Learning (FL) has become an important learning paradigm. However, jointly learning a deep …
Federated domain generalization: A survey
Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …
identical and that data is centrally stored for training and testing. However, in real-world …