mlsanityData to 7 Oct 2026

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

arXiv 2401.12356 ↗First posted 22 Jan 2024cs.LG cs.DC cs.GTcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 22 Jan 2024.

Builds on

9 of 20 references.

References · 9

  1. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  2. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  3. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  4. 2023A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Datacited by 14
  5. 2022Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networkscited by 12
  6. 2021Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Datacited by 10
  7. 2023Federated Learning for Water Consumption Forecasting in Smart Citiescited by 7
  8. 2022A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networkscited by 6
  9. 2023Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learningcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systemscited by 0
  2. 2026FedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated Learningcited by 0
  3. 2026Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systemscited by 0