The paper introduces COIN-GP, an observer‑based cooperative learning framework for distributed sensor networks that jointly estimates system states and partially unknown dynamics using online distributed Gaussian Process regression. It addresses challenges of incomplete state observations and deficient GP models by proposing a novel data collection strategy with theoretical feasibility conditions. The authors also derive an error upper bound for both state and model estimation and demonstrate through simulations that COIN‑GP outperforms existing distributed GP‑based methods.
By Zewen Yang, Xiaobing Dai, Zhenxiao Yin, Hang Zhao, Zhijun Li, C. C. Chan
arXiv:2606. 28441v1 Announce Type: cross Abstract: Online latent state estimation constitutes a fundamental challenge within the artificial intelligence field, serving as a foundational tool for diverse applications, including sequential decision making, anomaly and change-point detection.
By George Stamatelis, Kyriakos Stylianopoulos, George C. Alexandropoulos
arXiv:2608.29562v1 Announce Type: new
Abstract: Ensuring the safe operation of multi-agent systems (MASs) under uncertain environments is crucial for cooperative robotic, where external disturbances...
By Xiaobing Dai, Zewen Yang, Wei Ren, Sandra Hirche
arXiv:2609.19337v1 Announce Type: new
Abstract: We present Personalized Federated Hierarchical Gaussian Processes (pFedHGP) for probabilistic regression and classification when data are distributed a...
By Xianjian Xie, Hao Yan
arXiv:2606. 02767v1 Announce Type: cross Abstract: Kalman filtering performance is highly sensitive to model mismatch and noise covariance tuning.
By Jiho Lee, Nisar R. Ahmed, Rebecca Russell
The paper introduces FedSWE, a federated learning algorithm designed to handle non‑stationary and heterogeneous client availability without requiring prior real‑time knowledge of which devices are online. FedSWE compensates for missed computations, stabilizes global updates, and mixes local updates through implicit gossiping, all while adding only modest memory and computational overhead. The authors prove that FedSWE converges to a stationary point for non‑convex objectives and achieves linear speedup in certain scenarios, and they validate these claims with experiments on real‑world datasets featuring diverse client unavailability patterns.
By Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su
Tsetlin Machine (TM) is a rule-based machine-learning algorithm comprising collectives of two-action Tsetlin Automata (TAs) that cooperatively form conjunctive logical clauses from Boolean inputs through stochastic feedback. Although few recent studies have examined TM Federated Learning, the broader area of distributed and decentralized TM learning has not received much attention in the existing literature and warrants further exploration.
arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).
By Ferdous Pervej, Minseok Choi, Andreas F. Molisch
arXiv:2603. 01730v2 Announce Type: replace Abstract: Decentralized federated learning (DFL) has emerged as a transformative server-free paradigm that enables collaborative learning over large-scale heterogeneous networks.
By Shan Sha, Shenglong Zhou, Xin Wang, Lingchen Kong, Geoffrey Ye Li
arXiv:2609.07312v1 Announce Type: new
Abstract: This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attack...
By Xiao Ma, Hong Shen, Hui Tian, Wenqi Lyu, Wei Ke
arXiv:2504. 12742v2 Announce Type: replace Abstract: Decentralized Federated Learning (DFL) enables collaborative model training without relying on a central server.
By Yuan Zhou, Xinli Shi, Xuelong Li, Jiachen Zhong, Guanghui Wen, Jinde Cao
arXiv:2607. 20124v1 Announce Type: new Abstract: Tsetlin Machine (TM) is a rule-based machine-learning algorithm comprising collectives of two-action Tsetlin Automata (TAs) that cooperatively form conjunctive logical clauses from Boolean inputs through stochastic feedback.
By Yehuda Rudin, Osnat Keren, Michal Yemini, Alexander Fish