Coalition Formation

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13 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems
Sep 17, 2026Mohammed El Hanjri, Anas Abouaomar, Hamidou Tembine +1Opinion Dynamics Federated Learning Aggregation
ENSIAS, Mohammed V University in Rabat, Morocco. · Department of Electrical and Computer Engineering, School of Engineering, University of Quebec at Trois-Rivieres, Quebec, Canada.
Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach
Jul 29, 2026Cengis HasanCooperative Game Theory Coalition Formation
Cognifinity, Luxembourg
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Jul 16, 2026Dylan Van Mulders, Matthias Bogaert, Dirk Van den PoelMulti-Agent Negotiation Automated Negotiation
Ghent University CVAMO Core Lab
ANet Patu-1: The Value of Connection in the Agent Network
Jul 16, 2026Mu Yuan, Jinke Song, Zhaomeng Zhou +1Multi-Agent Coordination Agent Communication Protocols
Agent Network Research · The Chinese University of Hong Kong · Mu Yuan1,2 +5
Exit-and-Join Dynamics and Equilibrium in Continuum Cooperative Games
Jun 27, 2026Quanyan ZhuCooperative Game Theory Coalition Formation
Department of Electrical and Computer Engineering · New York University Tandon School of Engineering · Brooklyn, NY, USA
Multiscale Exit-Join Dynamics: Tactical Consensus and Strategic Coalition Formation
Jun 19, 2026Quanyan ZhuCooperative Game Theory Multi-Agent Coordination
Department of Electrical and Computer Engineering New York University Tandon School of Engineering Brooklyn, NY, USA
Exit-and-Join Dynamics for Decentralized Coalition Formation
Jun 18, 2026Quanyan ZhuCooperative Game Theory Coalition Formation
Department of Electrical and Computer Engineering New York University Tandon School of Engineering Brooklyn, NY, USA
Nash Welfare in Additively Separable Hedonic Games
May 18, 2026Marta Pagano, Alexander SchlengaCooperative Game Theory Game Theory
Sapienza University of Rome, Italy · Technical University of Munich, Germany
From Necklaces to Coalitions: Fair and Self-Interested Distribution of Coalition Value Calculations
Apr 18, 2026Terry R. Payne, Luke RileyCooperative Game Theory Multi-Agent Coordination
School of Computer Science and Informatics, University of Liverpool, Liverpool, UK. · Quant, London, UK.
Coalition Formation in LLM Agent Networks: Stability Analysis and Convergence Guarantees
Apr 15, 2026Dongxin Guo, Jikun Wu, Siu-Ming YiuCoalition Formation Multi-Agent LLM Systems
Department of Compute Science, The University of Hong Kong, Hong Kong, China · Brain Investing Limited, Hong Kong, China · Stellaris AI Limited, Hong Kong, China
Visualizing Coalition Formation: From Hedonic Games to Image Segmentation
Mar 9, 2026Pedro Henrique de Paula França, Lucas Lopes Felipe, Daniel Sadoc MenaschéCoalition Formation Cooperative Game Theory
UFRJ
Stability Under Valuation Updates in Coalition Formation
Feb 24, 2026Fabian Frank, Matija Novaković, René RomenCoalition Formation Cooperative Game Theory
Technical University of Munich
Control in Hedonic Games
Feb 18, 2026Jiehua Chen, Jakob Guttmann, Merisa Mustajbašić +1Coalition Formation Game Theory
TU Wien Vienna, Austria

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Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems

Mohammed El Hanjri, Anas Abouaomar, Hamidou Tembine, Abdellatif Kobbane

ENSIAS, Mohammed V University in Rabat, Morocco. · Department of Electrical and Computer Engineering, School of Engineering, University of Quebec at Trois-Rivieres, Quebec, Canada.

Opinion Dynamics Federated Learning Aggregation Coalition Formation Time Series Forecasting Clustered Federated Learning Heterogeneous Federated Learning

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Federated learning (FL) enables privacy-preserving, on-device training across heterogeneous Internet-of-Things (IoT) deployments such as smart-city water-metering networks, where each smart meter observes a household-specific consumption time series. Under such statistical heterogeneity, the standard Federated Averaging (FedAvg) aggregation averages dissimilar local models into a single global model that may fail to capture client-specific patterns. We address this by forming client coalitions directly in the local-weight space and aggregating at the coalition level. Extending a prior weight-driven coalition-formation scheme, we model coalition formation as a Hegselmann-Krause (HK) bounded-confidence opinion-dynamics process acting on the local weights, and develop variants of the HK interaction based on Euclidean-distance and cosine-similarity confidence criteria. The framework is applied to short-term water-consumption forecasting with local Long Short-Term Memory (LSTM) models and evaluated against FedAvg, Per-FedAvg, FedProx, and FedAvg with Euclidean-distance or cosine-similarity coalition formation. Experiments on a real smart-metering dataset of water consumption show that the proposed HK-based coalition formation produces stable, endogenous coalition structures within at most ten inner iterations, incurs no additional client-side computation or communication compared to FedAvg, and reduces the average MAE by up to 54% relative to FedAvg, 39% relative to FedProx, and 24% relative to Per-FedAvg, while achieving the highest global accuracy (83-85%).