# Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems

arXiv: https://arxiv.org/abs/2401.12356 · HTML: https://mlsanity.com/paper/2401.12356/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data](https://mlsanity.com/paper/2309.01275/) (2023)
- [Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks](https://mlsanity.com/paper/2206.11328/) (2022)
- [Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data](https://mlsanity.com/paper/2107.06548/) (2021)
- [Federated Learning for Water Consumption Forecasting in Smart Cities](https://mlsanity.com/paper/2301.13036/) (2023)
- [A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks](https://mlsanity.com/paper/2201.11831/) (2022)
- [Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning](https://mlsanity.com/paper/2308.02614/) (2023)

## Built on by (most cited first, 3 of 3)

- [Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems](https://mlsanity.com/paper/2510.12727/) (2025)
- [FedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated Learning](https://mlsanity.com/paper/2603.04422/) (2026)
- [Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems](https://mlsanity.com/paper/2609.19695/) (2026)

## Abstract (arXiv)

In the era of the Internet of Things (IoT), decentralized paradigms for machine learning are gaining prominence. In this paper, we introduce a federated learning model that capitalizes on the Euclidean distance between device model weights to assess their similarity and disparity. This is foundational for our system, directing the formation of coalitions among devices based on the closeness of their model weights. Furthermore, the concept of a barycenter, representing the average of model weights, helps in the aggregation of updates from multiple devices. We evaluate our approach using homogeneous and heterogeneous data distribution, comparing it against traditional federated learning averaging algorithm. Numerical results demonstrate its potential in offering structured, outperformed and communication-efficient model for IoT-based machine learning.