14 citations · 23 across the 9 of their papers we have counts for
9 papers
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…
Federated Graph Learning with Adaptive Importance-based Sampling
Anran Li, Yuanyuan Chen, Chao Ren +5
For privacy-preserving graph learning tasks involving distributed graph datasets, federated learning (FL)-based GCN (FedGCN) training is required. A key challenge for FedGCN is sca…
Federated Model Heterogeneous Matryoshka Representation Learning
Liping Yi, Han Yu, Chao Ren +3
Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHet…
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
Liping Yi, Han Yu, Chao Ren +4
Model-heterogeneous personalized federated learning (MHPFL) enables FL clients to train structurally different personalized models on non-independent and identically distributed (n…
Advances and Open Challenges in Federated Foundation Models
Chao Ren, Han Yu, Hongyi Peng +9
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabil…
Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks
Yulan Gao, Chao Ren, Han Yu
In the rapidly advancing field of federated learning (FL), ensuring efficient FL task delegation while incentivising FL client participation poses significant challenges, especiall…
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
Liping Yi, Han Yu, Chao Ren +4
Federated learning (FL) has been widely adopted for collaborative training on decentralized data. However, it faces the challenges of data, system, and model heterogeneity. This ha…
Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation
Minrui Xu, Dusit Niyato, Jiawen Kang +5
Quantum computing networks enable scalable collaboration and secure information exchange among multiple classical and quantum computing nodes while executing large-scale generative…
Towards Quantum Federated Learning
Chao Ren, Rudai Yan, Huihui Zhu +9
Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveragin…