1 citations · 2 across the 6 of their papers we have counts for
6 papers
ZeroLock: Concurrent Memory-Efficient LLM Training via Modular Update Decoupling
Wentao Dai, Xuanran Li, Yuxiang Zhang +2
Large language model (LLM) fine-tuning at the edge adapts the model to scenario-specific data while preserving privacy. Although existing studies proposed pipeline parallelism to a…
Decoupled Split Learning via Auxiliary Loss
Anower Zihad, Felix Owino, Ming Tang +1
Split learning is a distributed training paradigm where a neural network is partitioned between clients and a server, which allows data to remain at the client while only intermedi…
FlowSpec: Continuous Pipelined Speculative Decoding for Efficient Distributed LLM Inference
Xing Liu, Lizhuo Luo, Ming Tang +2
Distributed inference serves as a promising approach to enabling the inference of large language models (LLMs) at the network edge. It distributes the inference process to multiple…
CoCoI: Distributed Coded Inference System for Straggler Mitigation
Xing Liu, Chao Huang, Ming Tang
Convolutional neural networks (CNNs) are widely applied in real-time applications on resource-constrained devices. To accelerate CNN inference, prior works proposed to distribute t…
Convergence Analysis of Split Federated Learning on Heterogeneous Data
Pengchao Han, Chao Huang, Geng Tian +2
Split federated learning (SFL) is a recent distributed approach for collaborative model training among multiple clients. In SFL, a global model is typically split into two parts, w…
Price of Stability in Quality-Aware Federated Learning
Yizhou Yan, Xinyu Tang, Chao Huang +1
Federated Learning (FL) is a distributed machine learning scheme that enables clients to train a shared global model without exchanging local data. The presence of label noise can…