6 citations · 6 across the 2 of their papers we have counts for
10 papers
DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices
Songyuan Li, Jia Hu, Ahmed M. Abdelmoniem +3
Recent Mixture-of-Experts (MoE)-based large language models (LLMs) such as Qwen-MoE and DeepSeek-MoE are transforming generative AI in natural language processing. However, these m…
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection
Desong Zhang, Jia Hu, Geyong Min
Spiking Neural Networks (SNNs) process information via discrete spikes, enabling them to operate at remarkably low energy levels. However, our experimental observations reveal a st…
Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning
Feng Yu, Jia Hu, Geyong Min
Federated Parameter-Efficient Fine-Tuning (Fed-PEFT) enables lightweight adaptation of large pre-trained models in federated learning settings by updating only a small subset of pa…
Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation
Fei Wu, Jia Hu, Geyong Min +1
Driven by the rapid growth of model parameters, parameter-efficient fine-tuning (PEFT) has become essential for adapting large models to diverse downstream tasks under constrained…
Zero-Knowledge Proof-Based Consensus for Blockchain-Secured Federated Learning
Tianxing Fu, Jia Hu, Geyong Min +1
Federated learning (FL) enables multiple participants to collaboratively train machine learning models while ensuring their data remains private and secure. Blockchain technology f…
Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge
Songyuan Li, Jia Hu, Geyong Min +1
Foundation models (FMs) such as GPT-4 exhibit exceptional generative capabilities across diverse downstream tasks through fine-tuning. Split Federated Learning (SFL) facilitates pr…