5 papers
DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data
Yunsheng Yuan, Xue Xiao, Lina Wang +1
In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server. Although the power of collabor…
DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data
Yunsheng Yuan, Shaowei Li, Kai Wang +5
Fine-tuning large language models (LLMs) in privacy-sensitive and resource-constrained environments remains challenging. Since training data are often distributed across multiple c…
FGRPO: Federated GRPO with Adaptive Aggregation on Non-IID Data
Pengyu Chen, Shaowei Li, Kai Wang +4
Recent advances in language models have established reinforcement learning as the primary paradigm for eliciting self-correction and long-chain reasoning. While group relative poli…
ROSS: RObust decentralized Stochastic learning based on Shapley values
Lina Wang, Yunsheng Yuan, Feng Li +1
In the paradigm of decentralized learning, a group of agents collaborate to learn a global model using a distributed dataset without a central server; nevertheless, it is severely…
PDSL: Privacy-Preserved Decentralized Stochastic Learning with Heterogeneous Data Distribution
Lina Wang, Yunsheng Yuan, Chunxiao Wang +1
In the paradigm of decentralized learning, a group of agents collaborates to learn a global model using distributed datasets without a central server. However, due to the heterogen…