arXiv Machine Learning

MMFGU: Multimodal Federated Graph Unlearning

arXiv:2607. 28708v1 Announce Type: new Abstract: Multimodal federated graph learning enables clients to collaboratively train graph models over structural, textual, and visual signals without sharing private local data.

arXiv Machine Learning

Jul 31

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

arXiv:2607. 27665v1 Announce Type: new Abstract: Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information.

By Zekai Chen, Haodong Lu, Shihao Li, Weiwei Ji, Xunkai Li, Xun Wu, Yinlin Zhu, Rong-Hua Li

efficiency multimodal

arXiv Machine Learning

Jul 20

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

arXiv:2607. 15687v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs.

By Xunkai Li, Guohao Fu, Yuming Ai, Zhengyu Wu, Hongchao Qin, Rong-Hua Li, Guoren Wang

fine-tuning multimodal safety

arXiv Machine Learning

Jul 14

Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion

arXiv:2506. 22036v2 Announce Type: replace Abstract: With the increasing multimodal knowledge privatization requirements, multimodal knowledge graphs in different institutes are usually decentralized, lacking of effective collaboration system with both stronger reasoning ability and transmission safety guarantees.

By Ying Zhang, Yu Zhao, Xuhui Sui, Baohang Zhou, Xiangrui Cai, Li Shen, Xiaojie Yuan, Dacheng Tao

rag diffusion efficiency multimodal benchmarks safety

arXiv Machine Learning

Jun 19

Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach

arXiv:2606. 20382v1 Announce Type: new Abstract: MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance.

By Zhengyu Wu, Hongchao Qin, Xunkai Li, Zekai Chen, Rong-Hua Li, Guoren Wang

multimodal safety

arXiv Machine Learning

Aug 4

Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

arXiv:2608. 00623v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains.

By Yinlin Zhu, Di Wu, Yi Zhang, Xunkai Li, Wang Luo, Wei-Jin Huang, Miao Hu, Guocong Quan

fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 1

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning

arXiv:2606. 32016v1 Announce Type: new Abstract: Multimodal graph foundation models aim to learn reusable knowledge from graphs enriched with text, images, attributes, and relational topology, thereby supporting diverse graph-centric and modality-centric tasks.

By Zekai Chen, Kairui Yang, Xuaner Chen, Xunkai Li, Xun Wu, Rong-Hua Li, Guoren Wang

rag multimodal benchmarks safety

arXiv AI

Jul 8

MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning

arXiv:2409. 06067v3 Announce Type: replace Abstract: Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients.

By Jianyi Zhang, Hao Frank Yang, Ang Li, Xin Guo, Pu Wang, Haiming Wang, Yiran Chen, Hai Li

llms nlp multimodal benchmarks safety

arXiv Machine Learning

Jun 9

PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

arXiv:2606. 09301v1 Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images.

By Zekai Chen, Miao Zhang, Jiayang Xing, Xunkai Li, Xun Wu, Rong-Hua Li, Guoren Wang

llms multimodal benchmarks

arXiv AI

Aug 7

TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning

arXiv:2604. 02183v3 Announce Type: replace Abstract: Multimodal recommendation systems (MRS) jointly model user-item interaction graphs and rich item content, but this tight coupling makes user data difficult to remove once learned.

By Zhanting Zhou, KaHou Tam, Ziqiang Zheng, Zeyu Ma, Yang Yang

multimodal

arXiv AI

Jun 2

Boosting Multimodal Federated Learning via Chained Modality Optimization

arXiv:2606. 01856v1 Announce Type: cross Abstract: Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative learning across decentralized clients with heterogeneous data and modality availability.

By Zixin Zhang, Fan Qi, Shuai Li, Xiaoshan Yang, Changsheng Xu

multimodal benchmarks safety

arXiv Machine Learning

Jul 2

FedIA: Importance-Aware Aggregation for Domain-Robust Federated Graph Learning

arXiv:2509. 18171v4 Announce Type: replace Abstract: Federated graph learning (FGL) is a natural paradigm for social-media user graphs, where language communities, regional markets, and service boundaries can prevent raw graph pooling.

By Zhanting Zhou, Zeyu Ma, Ziqiang Zheng, Yang Yang

benchmarks

arXiv Machine Learning

Jul 28

FedTaste: Topology-Aware Structural Transfer for Multimodal Federated Learning with Missing Modalities

arXiv:2607. 23245v1 Announce Type: cross Abstract: Multimodal Federated Learning is often challenged by arbitrary modality missingness and Non-IID data distributions, which lead to severe representation drift and hinder effective collaboration across clients.

By Haochen Liang, Jie Zhang, Hideya Ochiai

multimodal safety

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