FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data
Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from
decentralized local clients manifests a globally prevalent long-tailed distribution, has …
decentralized local clients manifests a globally prevalent long-tailed distribution, has …
FedSheafHN: Personalized Federated Learning on Graph-structured Data
Personalized subgraph Federated Learning (FL) is a task that customizes Graph Neural
Networks (GNNs) to individual client needs, accommodating diverse data distributions …
Networks (GNNs) to individual client needs, accommodating diverse data distributions …
Decentralized Personalized Federated Learning
This work tackles the challenges of data heterogeneity and communication limitations in
decentralized federated learning. We focus on creating a collaboration graph that guides …
decentralized federated learning. We focus on creating a collaboration graph that guides …
Advances in Robust Federated Learning: Heterogeneity Considerations
C Chen, T Liao, X Deng, Z Wu, S Huang… - arXiv preprint arXiv …, 2024 - arxiv.org
In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …
collaboratively train models across multiple clients with different data distributions, model …
FedCache 2.0: Exploiting the Potential of Distilled Data in Knowledge Cache-driven Federated Learning
Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge
devices to collaboratively train machine learning models while preserving data privacy …
devices to collaboratively train machine learning models while preserving data privacy …