6 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…
Rethinking the Personalized Relaxed Initialization in the Federated Learning: Consistency and Generalization
Li Shen, Yan Sun, Dacheng Tao
Federated learning (FL) is a distributed paradigm that coordinates massive local clients to collaboratively train a global model via stage-wise local training processes on the hete…
Stability and Generalization of Push-Sum Based Decentralized Optimization over Directed Graphs
Yifei Liang, Yan Sun, Xiaochun Cao +1
Push-Sum-based decentralized learning enables optimization over directed communication networks, where information exchange may be asymmetric. While convergence properties of such…
MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs
Yan Sun, Qixin Zhang, Zhiyuan Yu +3
The rapid scaling of large language models~(LLMs) has made inference efficiency a primary bottleneck in the practical deployment. To address this, semi-structured sparsity offers a…
A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models
Zihao Lin, Samyadeep Basu, Mohammad Beigi +18
The rise of foundation models has transformed machine learning research, prompting efforts to uncover their inner workings and develop more efficient and reliable applications for…
AdaGC: Enhancing LLM Pretraining Stability via Adaptive Gradient Clipping
Guoxia Wang, Shuai Li, Congliang Chen +5
Loss spikes remain a persistent obstacle in large-scale language model pretraining. While previous research has attempted to identify the root cause of loss spikes by investigating…