3 papers
cs.LG2025
Communication and Computation Efficient Split Federated Learning in O-RAN
Shunxian Gu, Chaoqun You, Bangbang Ren +1
The hierarchical architecture of Open Radio Access Network (O-RAN) has enabled a new Federated Learning (FL) paradigm that trains models using data from non- and near-real-time (ne…
cs.LG2025
DHO: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM
Shunxian Gu, Chaoqun You, Bangbang Ren +3
Scaling deep neural network (DNN) training to more devices can reduce time-to-solution. However, it is impractical for users with limited computing resources. FOSI, as a hybrid ord…
cs.DC2025
Analytic Personalized Federated Meta-Learning
Shunxian Gu, Chaoqun You, Deke Guo +4
Analytic Federated Learning (AFL) is an enhanced gradient-free federated learning (FL) paradigm designed to accelerate training by updating the global model in a single step with c…