5 papers
Beyond Factor Aggregation: Gauge-Aware Low-Rank Server Representations for Federated LoRA
Jinqian Chen, Chang Liu, Jihua Zhu
Federated LoRA enables parameter-efficient adaptation of large language models under decentralized data and limited client resources.However, directly averaging LoRA factors is rep…
FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
Chaoyi Lu, Yiding Sun, Zhichuan Yang +3
Asynchronous Federated Learning (AFL) has emerged as a significant research area in recent years. By not waiting for slower clients and executing the training process concurrently,…
AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning
Chaoyi Lu, Yiding Sun, Jinqian Chen +3
Asynchronous federated learning (AFL) accelerates training by eliminating the need to wait for stragglers, but its asynchronous nature introduces gradient staleness, where outdated…
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast
Yiting Zheng, Bohan Lin, Jinqian Chen +1
Most current federated learning frameworks are modeled as static processes, ignoring the dynamic characteristics of the learning system. Under the limited communication budget of t…
TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic
Jinqian Chen, Jihua Zhu
Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy. Despite its widespread adoption, most FL approaches focusing…