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

arXiv: https://arxiv.org/abs/2609.19695 · HTML: https://mlsanity.com/paper/2609.19695/

## 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.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [Building Energy Load Forecasting using Deep Neural Networks](https://mlsanity.com/paper/1610.09460/) (2016)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data](https://mlsanity.com/paper/2309.01275/) (2023)
- [Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach](https://mlsanity.com/paper/2411.10619/) (2024)
- [FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering](https://mlsanity.com/paper/2407.07124/) (2024)
- [A Federated DRL Approach for Smart Micro-Grid Energy Control with Distributed Energy Resources](https://mlsanity.com/paper/2211.03430/) (2022)
- [Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning](https://mlsanity.com/paper/2502.17226/) (2025)
- [DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game](https://mlsanity.com/paper/2412.15492/) (2024)
- [Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients](https://mlsanity.com/paper/2309.13194/) (2023)
- [Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems](https://mlsanity.com/paper/2401.12356/) (2024)

## 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%).