The paper proposes an opinion‑dynamics approach to form client coalitions in federated learning for heterogeneous IoT systems, specifically targeting smart‑meter water‑consumption forecasting. By modeling coalition formation as a Hegselmann‑Krause bounded‑confidence process on local model weights, the method creates stable coalition structures within ten iterations without extra client computation or communication. Experiments on a real smart‑metering dataset show that this HK‑based coalition formation reduces mean absolute error by up to 54% compared to FedAvg and achieves the highest global accuracy (83‑85%).
By Mohammed El Hanjri, Anas Abouaomar, Hamidou Tembine, Abdellatif Kobbane
The paper investigates how model initialization affects federated short‑term load forecasting (STLF) when client data are heterogeneous. It introduces two strategies: a global pretrained initialization using auxiliary public load data to reduce client drift, and a local sequential initialization (SLIAvg) that lets clients start from progressively adapted models each round. Experiments on real smart‑meter data show these methods improve convergence and lower forecasting errors while remaining compatible with existing federated learning frameworks.
By Jianing Chen, Vajiheh Farhadi, Yan Li, Thomas La Porta
arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.
By Hai Anh Tran, Cuong Ta, Truong X. Tran
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:2506. 02897v3 Announce Type: replace Abstract: Federated Learning (FL) enables privacy-preserving collaborative model training, but its effectiveness is often limited by client data heterogeneity.
By Alessandro Licciardi, Roberta Raineri, Anton Proskurnikov, Lamberto Rondoni, Lorenzo Zino
arXiv:2606. 18003v1 Announce Type: cross Abstract: Collective Adaptive Systems (CAS) increasingly rely on machine learning to let each node learn from locally sensed data, aligning its behavior with the surrounding environment.
By Davide Domini, Gianluca Aguzzi, Lorenzo Pellegrini, Mirko Viroli, Lukas Esterle