1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2024
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…
cs.LG2024★ 1 cited
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…