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cs.LG2026
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…
cs.DC2026
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…
cs.LG2026
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…