11 citations · 11 across the 5 of their papers we have counts for
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cs.LG2026
FedUMM: A General Framework for Federated Learning with Unified Multimodal Models
Zhaolong Su, Leheng Zhao, Xiaoying Wu +2
Unified multimodal models (UMMs) are emerging as strong foundation models that can do both generation and understanding tasks in a single architecture. However, they are typically…
cs.LG2024
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning
Ziyue Xu, Mingfeng Xu, Tianchi Liao +2
Recently, the success of large models has demonstrated the importance of scaling up model size. This has spurred interest in exploring collaborative training of large-scale models…
cs.LG2021★ 11 cited
FedGL: Federated Graph Learning Framework with Global Self-Supervision
Chuan Chen, Weibo Hu, Ziyue Xu +1
Graph data are ubiquitous in the real world. Graph learning (GL) tries to mine and analyze graph data so that valuable information can be discovered. Existing GL methods are design…