6 papers
FedOptima: Optimizing Resource Utilization in Federated Learning
Zihan Zhang, Leon Wong, Blesson Varghese
Federated learning (FL) systems facilitate distributed machine learning across a server and multiple devices. However, FL systems have low resource utilization on servers and devic…
MARS-Sep: Multimodal-Aligned Reinforced Sound Separation
Zihan Zhang, Xize Cheng, Zhennan Jiang +4
Universal sound separation faces a fundamental misalignment: models optimized for low-level signal metrics often produce semantically contaminated outputs, failing to suppress perc…
M2XFP: A Metadata-Augmented Microscaling Data Format for Efficient Low-bit Quantization
Weiming Hu, Zihan Zhang, Haoyan Zhang +8
Existing low-bit Microscaling (MX) formats, such as MXFP4, often suffer from substantial accuracy degradation due to the use of a shared scaling factor with the Power-of-Two format…
GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation
Xinghe Cheng, Zihan Zhang, Jiapu Wang +5
Learning path recommendation seeks to provide learners with a structured sequence of learning items (\eg, knowledge concepts or exercises) to optimize their learning efficiency. De…
Ampere: Communication-Efficient and High-Accuracy Split Federated Learning
Zihan Zhang, Leon Wong, Blesson Varghese
A Federated Learning (FL) system collaboratively trains neural networks across devices and a server but is limited by significant on-device computation costs. Split Federated Learn…
Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning
Zhu Xu, Zhiqiang Zhao, Zihan Zhang +6
Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens. T…