52 citations · 52 across the 4 of their papers we have counts for
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cs.LG2026
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
Jing Liu, Chenxuanyin Zou, Jiayang Ren +5
Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obsta…
cs.LG2026
PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs
Jing Liu, Kun Yang, Yan Wang +5
Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at networ…
cs.CV2026
Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity
Jing Liu, Zhengliang Guo, Yan Wang +4
Federated learning (FL) is severely challenged by non-independent and identically distributed (non-IID) client data, a problem that degrades global model performance, especially in…