A survey on federated learning systems: Vision, hype and reality for data privacy and protection
As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …
been a hot research topic in enabling the collaborative training of machine learning models …
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 …
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 …
FedCPF: An efficient-communication federated learning approach for vehicular edge computing in 6G communication networks
The sixth-generation network (6G) is expected to achieve a fully connected world, which
makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging …
makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging …
Fedbabu: Towards enhanced representation for federated image classification
Federated learning has evolved to improve a single global model under data heterogeneity
(as a curse) or to develop multiple personalized models using data heterogeneity (as a …
(as a curse) or to develop multiple personalized models using data heterogeneity (as a …
Collaborative unsupervised visual representation learning from decentralized data
Unsupervised representation learning has achieved outstanding performances using
centralized data available on the Internet. However, the increasing awareness of privacy …
centralized data available on the Internet. However, the increasing awareness of privacy …
Divergence-aware federated self-supervised learning
Self-supervised learning (SSL) is capable of learning remarkable representations from
centrally available data. Recent works further implement federated learning with SSL to …
centrally available data. Recent works further implement federated learning with SSL to …
Deep learning-based person re-identification methods: A survey and outlook of recent works
In recent years, with the increasing demand for public safety and the rapid development of
intelligent surveillance networks, person re-identification (Re-ID) has become one of the hot …
intelligent surveillance networks, person re-identification (Re-ID) has become one of the hot …
When foundation model meets federated learning: Motivations, challenges, and future directions
The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …
Federated learning for non-iid data via unified feature learning and optimization objective alignment
Federated Learning (FL) aims to establish a shared model across decentralized clients
under the privacy-preserving constraint. Despite certain success, it is still challenging for FL …
under the privacy-preserving constraint. Despite certain success, it is still challenging for FL …