2 citations · 2 across the 5 of their papers we have counts for
7 papers
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…
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness
Mengyao Du, Miao Zhang, Yuwen Pu +3
To tackle the scarcity and privacy issues associated with domain-specific datasets, the integration of federated learning in conjunction with fine-tuning has emerged as a practical…
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…