4 papers
Fisher-Informed Parameterwise Aggregation for Federated Learning with Heterogeneous Data
Zhipeng Chang, Ting He, Wenrui Hao
Federated learning aggregates model updates from distributed clients, but standard first order methods such as FedAvg apply the same scalar weight to all parameters from each clien…
Optimizing Resource Allocation for Geographically-Distributed Inference by Large Language Models
Tingyang Sun, Ting He, Bo Ji +1
Large language models have demonstrated extraordinary performance in many AI tasks but are expensive to use, even after training, due to their requirement of high-end GPUs. Recentl…
S2M3: Split-and-Share Multi-Modal Models for Distributed Multi-Task Inference on the Edge
JinYi Yoon, JiHo Lee, Ting He +2
With the advancement of Artificial Intelligence (AI) towards multiple modalities (language, vision, speech, etc.), multi-modal models have increasingly been used across various app…
Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks
Tingyang Sun, Tuan Nguyen, Ting He
Decentralized federated learning (DFL) is a promising machine learning paradigm for bringing artificial intelligence (AI) capabilities to the network edge. Running DFL on top of ed…