6 papers
Optimizing Stochastic Gradient Push under Broadcast Communications
Tuan Nguyen, Ting He
We consider the problem of minimizing the convergence time for decentralized federated learning (DFL) in wireless networks under broadcast communications, with focus on mixing matr…
Serving Chain-structured Jobs with Large Memory Footprints with Application to Large Foundation Model Serving
Tingyang Sun, Ting He, I-Hong Hou
As a current trend in Artificial Intelligence (AI), large foundation models are increasingly employed as the core of AI services. However, even after training, serving such models…
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…