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
The paper introduces two distributed recursive Gaussian process algorithms, ADMM‑RGP and PDMM‑RGP, designed for multi‑output regression in multi‑agent systems. It analyzes their stability and convergence, proposes parameter selection strategies to speed up convergence, and demonstrates that the methods reduce communication overhead while preserving estimation accuracy and consensus. Experiments on a real‑world wind dataset confirm the algorithms’ effectiveness across different communication graph connectivities.
By Josephine King, Ali Emre Balci, Raj Thilak Rajan
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
The paper introduces Personalized Federated Hierarchical Gaussian Processes (pFedHGP), a method for probabilistic regression and classification on data distributed across heterogeneous clients. Each client’s latent function is split into a shared global component, a client‑specific deviation that shares the global kernel, and a flexible local residual. Using sparse inducing‑variable approximations and federated variational inference, raw data remain local while the server exchanges only low‑dimensional statistics, enabling full predictive distributions for uncertainty‑aware decisions. In experiments, pFedHGP achieves perfect fault classification in press tonnage monitoring with only 13.77% of labeled cycles and accurately recovers geographic zones in federated air‑quality modeling without centralizing station‑level time series.
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
arXiv:2609.39250v1 Announce Type: new
Abstract: Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a...
By Muzaffer Citir, Hiroki Nishikawa, Sangyoung Park
Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a cloud server. Straggling clients have been a prob...
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
In traditional Gaussian process (GP) regression, the inputs are typically assumed to be known, and uncertainty enters only through the measurements. When scalar fields are mapped using GP regression w...