Multimodal federated learning: A survey
Federated learning (FL), which provides a collaborative training scheme for distributed data
sources with privacy concerns, has become a burgeoning and attractive research area. Most …
sources with privacy concerns, has become a burgeoning and attractive research area. Most …
Towards personalized federated learning via heterogeneous model reassembly
This paper focuses on addressing the practical yet challenging problem of model
heterogeneity in federated learning, where clients possess models with different network …
heterogeneity in federated learning, where clients possess models with different network …
[PDF][PDF] Backdoor threats from compromised foundation models to federated learning
Federated learning (FL) represents a novel paradigm to machine learning, addressing
critical issues related to data privacy and security, yet suffering from data insufficiency and …
critical issues related to data privacy and security, yet suffering from data insufficiency and …
Position paper: Assessing robustness, privacy, and fairness in federated learning integrated with foundation models
Federated Learning (FL), while a breakthrough in decentralized machine learning, contends
with significant challenges such as limited data availability and the variability of …
with significant challenges such as limited data availability and the variability of …
Unveiling backdoor risks brought by foundation models in heterogeneous federated learning
The foundation models (FMs) have been used to generate synthetic public datasets for the
heterogeneous federated learning (HFL) problem where each client uses a unique model …
heterogeneous federated learning (HFL) problem where each client uses a unique model …
FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers
Y Liu, H Wu, J Qin - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
Recent advancements in deep learning have greatly improved the efficiency of auxiliary
medical diagnostics. However, concerns over patient privacy and data annotation costs …
medical diagnostics. However, concerns over patient privacy and data annotation costs …
Rethinking Personalized Federated Learning with Clustering-Based Dynamic Graph Propagation
Most existing personalized federated learning approaches are based on intricate designs,
which often require complex implementation and tuning. In order to address this limitation …
which often require complex implementation and tuning. In order to address this limitation …
FedLEGO: Enabling Heterogenous Model Cooperation via Brick Reassembly in Federated Learning
This paper focuses on addressing the practical yet challenging problem of model
heterogeneity in federated learning, where clients possess models with different network …
heterogeneity in federated learning, where clients possess models with different network …
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality
Federated learning (FL) has obtained tremendous progress in providing collaborative
training solutions for distributed data silos with privacy guarantees. However, few existing …
training solutions for distributed data silos with privacy guarantees. However, few existing …
Personalized Federated Semi-Supervised Learning with Black-Box Models
S Huang, SY Li, S Chen - 2023 IEEE International Conference …, 2023 - ieeexplore.ieee.org
Federated Semi-Supervised Learning alleviates the necessity for fully labeled data in
Federated Learning. However, it does not sufficiently prioritize model privacy or the …
Federated Learning. However, it does not sufficiently prioritize model privacy or the …