arXiv Machine Learning July 10, 2026 By Vikash Sathiamoorthy, Shuo Huai, Hao Kong, Di Liu, Wendy Yong Yi Loy, Christian Makaya, Daren Ho, Ravi Subramaniam, Qian Lin, Weichen Liu

FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection

Read the original on arXiv Machine Learning →

arXiv:2607. 08014v1 Announce Type: cross Abstract: Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models without sharing local data with other parties, hence preserving data privacy.

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

arXiv AI

Jul 23

SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection

arXiv:2604. 26633v2 Announce Type: replace-cross Abstract: Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly.

By Paul Julius K\"uhn, Mika Pommeranz, Arjan Kuijper, Saptarshi Neil Sinha

diffusion fine-tuning multimodal

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

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arXiv Machine Learning

Jun 25

FedReLa: Imbalanced Federated Learning via Re-Labeling

arXiv:2606. 26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation.

By Guangzheng Hu, Patricia Men\'endez, Feng Liu, Mingming Gong, Guanghui Wang, Liuhua Peng

safety

arXiv AI

6d ago

Federated Learning for Distributed CNC Tool Wear Prediction

arXiv:2608. 11281v1 Announce Type: cross Abstract: Tool wear prediction is an important task in CNC machining, where accurate monitoring of tool condition supports product quality and process reliability.

By Afsana Khan, Morris Stallmann, Marcin Pietrasik, Charis Kouzinopoulos, Anna Wilbik

arXiv Machine Learning

1d ago

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

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.

By Zhenyan Liu, Hua Zhang, Haoran Gao, Qi Li, Hongliang Zhu, Huiyu Zhou, Zongliang Shen, Yanxin Xu, Jiahui Wang

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arXiv Machine Learning

Jun 24

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective

arXiv:2512. 10244v2 Announce Type: replace-cross Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled and abundant unlabeled task-specific examples to annotate the unlabeled ones.

By Tian Liu, Anwesha Basu, James Caverlee, Shu Kong

llms fine-tuning multimodal benchmarks

Related stories

SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection

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

FedReLa: Imbalanced Federated Learning via Re-Labeling

Federated Learning for Distributed CNC Tool Wear Prediction

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

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective