41 citations · 229 across the 71 of their papers we have counts for
26 papers · 1 filter
HO-FL: Hybrid-Order Federated Learning for Heterogeneous Edge Devices
Qiyuan Chen, Xian Wu, Yanan Ma +1
Federated learning (FL) on memory-constrained edge devices faces a dilemma: first-order (FO) optimization (i.e., backpropagation) demands substantial memory, whereas zeroth-order (…
CFLoRA: Federated Fine-tuning of LLMs with Complementary Factors for Error-free Aggregation
Yanan Ma, Qiyuan Chen, Zihan Fang +2
Federated low-rank adaptation (LoRA) enables collaborative fine-tuning of large language models without centralizing private client data. Its factorized update, however, creates a…
SplitLite: Low-Rank Residual Compression for Split Learning
Tao Li, Yulin Tang, Qi Guo +1
Federated fine-tuning of on-device large language models (LLMs) faces a significant computing burden. To overcome this limitation, split learning (SL) has emerged as a promising so…
Efficient Resource Optimization for Split Federated Learning
Wei Wei, Xianhao Chen
Split federated learning (SFL) has emerged as a powerful paradigm for model training at the edge. However, SFL inherently involves discrete decision variables for model splitting a…
Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity
Zihan Fang, Qianru Wang, Haonan An +5
The growing demand for on-device large language model (LLM) services on mobile edge devices has driven the adoption of Mixture-of-Experts (MoE) architectures, which scale model cap…
GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems
Zheng Lin, Ons Aouedi, Zihan Fang +4
The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge devices. Parallel split learning (P…
HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
Qiyuan Chen, Xian Wu, Yi Wang +1
Fine-tuning large models on edge devices is severely hindered by the memory-intensive backpropagation (BP) in standard frameworks like federated learning and split learning. While…
HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts
Zihan Fang, Zheng Lin, Senkang Hu +5
While federated learning (FL) enables fine-tuning of large language models (LLMs) without compromising data privacy, the substantial size of an LLM renders on-device training impra…
SAFE-D: A Spatiotemporal Detection Framework for Abnormal Driving Among Parkinson's Disease-like Drivers
Hangcheng Cao, Baixiang Huang, Longzhi Yuan +4
A driver's health state serves as a determinant factor in driving behavioral regulation. Subtle deviations from normalcy can lead to operational anomalies, posing risks to public t…
SlimCaching: Edge Caching of Mixture-of-Experts for Distributed Inference
Qian Chen, Xianhao Chen, Kaibin Huang
Mixture-of-Experts (MoE) models improve the scalability of large language models (LLMs) by activating only a small subset of relevant experts per input. However, the sheer number o…
HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
Zheng Lin, Zhe Chen, Xianhao Chen +2
Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer-wise model partitioning. However, existing…
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
Zheng Lin, Yuxin Zhang, Zhe Chen +6
Recently, large language models (LLMs) have achieved remarkable breakthroughs, revolutionizing the natural language processing domain and beyond. Due to immense parameter sizes, fi…
Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective
Tianyang Duan, Zongyuan Zhang, Zheng Lin +7
Deep Reinforcement Learning (DRL) suffers from uncertainties and inaccuracies in the observation signal in realworld applications. Adversarial attack is an effective method for eva…
LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks
Zheng Lin, Yuxin Zhang, Zhe Chen +5
Recently, the increasing deployment of LEO satellite systems has enabled various space analytics (e.g., crop and climate monitoring), which heavily relies on the advancements in de…
FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks
Qian Chen, Xianhao Chen, Kaibin Huang
To bridge the digital divide, space-ground integrated networks (SGINs) are expected to deliver artificial intelligence (AI) services to every corner of the world. One key mission o…
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization
Zheng Lin, Wei Wei, Zhe Chen +4
As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated le…
SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
Zheng Lin, Xuanjie Hu, Yuxin Zhang +6
The scalability of large language models (LLMs) in handling high-complexity models and large-scale datasets has led to tremendous successes in pivotal domains. While there is an ur…
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
Zihan Fang, Zheng Lin, Zhe Chen +3
Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). However, for many downstream tasks,…
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
Zheng Lin, Guanqiao Qu, Wei Wei +2
The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated lear…
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
Guangyu Zhu, Yiqin Deng, Xianhao Chen +3
Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the res…
FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks
Zheng Lin, Zhe Chen, Zihan Fang +3
Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensor…
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
Zheng Lin, Guanqiao Qu, Qiyuan Chen +3
Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, t…
Optimal Resource Allocation for U-Shaped Parallel Split Learning
Song Lyu, Zheng Lin, Guanqiao Qu +3
Split learning (SL) has emerged as a promising approach for model training without revealing the raw data samples from the data owners. However, traditional SL inevitably leaks lab…
Split Learning in 6G Edge Networks
Zheng Lin, Guanqiao Qu, Xianhao Chen +1
With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence. Along this line, the proposal to incorp…