mlsanityData to 14 Oct 2026

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

arXiv 2609.19695 ↗First posted 17 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

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%).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 17 Sep 2026.

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15 of 33 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  5. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  6. 2016Building Energy Load Forecasting using Deep Neural Networkscited by 24
  7. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  8. 2023A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Datacited by 14
  9. 2024Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approachcited by 11
  10. 2024FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clusteringcited by 9
  11. 2022A Federated DRL Approach for Smart Micro-Grid Energy Control with Distributed Energy Resourcescited by 8
  12. 2025Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learningcited by 7
3 more
  1. 2024DualGFL: Federated Learning with a Dual-Level Coalition-Auction Gamecited by 5
  2. 2023Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clientscited by 3
  3. 2024Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systemscited by 3

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