arXiv:2601. 09304v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data.
By Sota Sugawara, Yuji Kawamata, Akihiro Toyoda, Tomoru Nakayama, Yukihiko Okada
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.
By Vikash Sathiamoorthy, Shuo Huai, Hao Kong, Di Liu, Wendy Yong Yi Loy, Christian Makaya, Daren Ho, Ravi Subramaniam, Qian Lin, Weichen Liu
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:2606. 16032v1 Announce Type: cross Abstract: Interest in applying data-driven approaches in manufacturing has grown significantly, particularly for mapping complex, high-dimensional relationships.
By Hadi Bakhshan, Sima Farshbaf, Fernando Rastellini, Josep Maria Carbonell
arXiv:2512. 24625v3 Announce Type: replace-cross Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management.
By Zijian Zhao, Yitong Shang, Sen Li
arXiv:2606. 03143v1 Announce Type: new Abstract: Modern LLM agents increasingly rely on skill libraries to handle complex tasks, making skill evolution a primary driver of self-improvement.
By Jingbo Yang, Guanyu Yao, Yang Zhang, Ramana Rao Kompella, Gaowen Liu, Shiyu Chang