2 citations · 3 across the 7 of their papers we have counts for
9 papers · 1 filter
Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs
Li Shen, Xiaolei Hao, Qinglun Li +3
One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm. However, under extremely non-IID set…
PACE: Parameter Change for Unsupervised Environment Design
Fang Yuan, Quanjun Yin, Siqi Shen +5
Unsupervised Environment Design (UED) offers a promising paradigm for improving reinforcement learning generalization by adaptively shaping training environments, but it requires r…
A Unified Generalization Framework for Model Merging: Trade-offs, Non-Linearity, and Scaling Laws
Qinglun Li, Anke Tang, Miao Zhang +3
Model merging efficiently aggregates capabilities from multiple fine-tuned models into a single one, operating purely in parameter space without original data or expensive re-compu…
Unveiling the Power of Multiple Gossip Steps: A Stability-Based Generalization Analysis in Decentralized Training
Qinglun Li, Yingqi Liu, Miao Zhang +3
Decentralized training removes the centralized server, making it a communication-efficient approach that can significantly improve training efficiency, but it often suffers from de…
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration
Qinglun Li, Miao Zhang, Yingqi Liu +3
Decentralized Federated Learning has emerged as an alternative to centralized architectures due to its faster training, privacy preservation, and reduced communication overhead. In…
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement
Qinglun Li, Miao Zhang, Mengzhu Wang +2
Decentralized Federated Learning (DFL) surpasses Centralized Federated Learning (CFL) in terms of faster training, privacy preservation, and light communication, making it a promis…
Decentralized Directed Collaboration for Personalized Federated Learning
Yingqi Liu, Yifan Shi, Qinglun Li +3
Personalized Federated Learning (PFL) is proposed to find the greatest personalized models for each client. To avoid the central failure and communication bottleneck in the server-…
Asymmetrically Decentralized Federated Learning
Qinglun Li, Miao Zhang, Nan Yin +2
To address the communication burden and privacy concerns associated with the centralized server in Federated Learning (FL), Decentralized Federated Learning (DFL) has emerged, whic…
DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning
Qinglun Li, Li Shen, Guanghao Li +2
To address the communication burden issues associated with federated learning (FL), decentralized federated learning (DFL) discards the central server and establishes a decentraliz…