arXiv Machine Learning August 18, 2026 By Zhenyan Liu, Hua Zhang, Haoran Gao, Qi Li, Hongliang Zhu, Huiyu Zhou, Zongliang Shen, Yanxin Xu, Jiahui Wang

FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting

Read the original on arXiv Machine Learning →

arXiv:2608. 15310v1 Announce Type: cross Abstract: Multimodal data collected by heterogeneous devices are used for collaborative training, where federated learning (FL) serves as a key paradigm for effective distributed modeling with data privacy preservation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

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

arXiv Machine Learning

1d ago

Global Federated Learning Strategies for Building Efficient Personalized Models

arXiv:2608. 15107v1 Announce Type: new Abstract: Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously.

By Seongyoon Kim

reinforcement-learning fine-tuning efficiency safety

arXiv AI

Aug 11

Multimodal Federated Learning under Dual-Axis Modality Missingness

arXiv:2608. 09240v1 Announce Type: cross Abstract: Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modality missingness: clients have different modality sets, and individual samples may contain only subsets of the modalities available locally.

By Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin, Yunqi Guo, Xiaomin Ouyang, Guoliang Xing, Steven Euijong Whang, Sung-Ju Lee

multimodal safety

arXiv Machine Learning

6d ago

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.

By Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay

arXiv Machine Learning

Jun 2

Closing the Alignment-Maturity Gap in Federated Prototype Learning

arXiv:2606. 02172v1 Announce Type: new Abstract: Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL).

By Mario Casado-Diez, Alejandro Dopico-Castro, Ver\'onica Bol\'on-Canedo, Bertha Guijarro-Berdi\~nas

rag benchmarks safety

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

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

Global Federated Learning Strategies for Building Efficient Personalized Models

Multimodal Federated Learning under Dual-Axis Modality Missingness

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

Closing the Alignment-Maturity Gap in Federated Prototype Learning