# Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services

arXiv: https://arxiv.org/abs/2009.02560 · HTML: https://mlsanity.com/paper/2009.02560/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 18 of 18)

- [Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning](https://mlsanity.com/paper/2203.09249/) (2022)
- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [A Systematic Survey of Blockchained Federated Learning](https://mlsanity.com/paper/2110.02182/) (2021)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions](https://mlsanity.com/paper/2110.04160/) (2021)
- [Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things](https://mlsanity.com/paper/2402.02506/) (2024)
- [Stylometry Analysis of Multi-authored Documents for Authorship and Author Style Change Detection](https://mlsanity.com/paper/2401.06752/) (2024)
- [Distributed Swarm Learning for Edge Internet of Things](https://mlsanity.com/paper/2403.20188/) (2024)
- [Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing](https://mlsanity.com/paper/2405.17782/) (2024)
- [Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective](https://mlsanity.com/paper/2405.20431/) (2024)
- [The Cost of Local and Global Fairness in Federated Learning](https://mlsanity.com/paper/2503.22762/) (2025)
- [SPRITE: A Scalable Privacy-Preserving and Verifiable Collaborative Learning for Industrial IoT](https://mlsanity.com/paper/2203.11914/) (2022)
- [Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data](https://mlsanity.com/paper/2204.14020/) (2022)
- [FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2307.08574/) (2023)
- [Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations](https://mlsanity.com/paper/2311.10832/) (2023)
- [Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case](https://mlsanity.com/paper/2404.14977/) (2024)
- [Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models](https://mlsanity.com/paper/2504.14126/) (2025)

## Abstract (arXiv)

Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical matter of performing efficient local training and inference at the edge devices. One of the main challenges for successful and efficient training and inference on edge devices is the careful selection of parameters to build local Machine Learning (ML) models. To this aim, we propose a Particle Swarm Optimization (PSO)-based technique to optimize the hyperparameter settings for the local ML models in an FL environment. We evaluate the performance of our proposed technique using two case studies. First, we consider smart city services and use an experimental transportation dataset for traffic prediction as a proxy for this setting. Second, we consider Industrial IoT (IIoT) services and use the real-time telemetry dataset to predict the probability that a machine will fail shortly due to component failures. Our experiments indicate that PSO provides an efficient approach for tuning the hyperparameters of deep Long short-term memory (LSTM) models when compared to the grid search method. Our experiments illustrate that the number of clients-server communication rounds to explore the landscape of configurations to find the near-optimal parameters are greatly reduced (roughly by two orders of magnitude needing only 2%--4% of the rounds compared to state of the art non-PSO-based approaches). We also demonstrate that utilizing the proposed PSO-based technique to find the near-optimal configurations for FL and centralized learning models does not adversely affect the accuracy of the models.