# Distributed Swarm Learning for Edge Internet of Things

arXiv: https://arxiv.org/abs/2403.20188 · HTML: https://mlsanity.com/paper/2403.20188/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 29 Mar 2024.

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

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services](https://mlsanity.com/paper/2009.02560/) (2020)
- [Joint Optimization of Communications and Federated Learning Over the Air](https://mlsanity.com/paper/2104.03490/) (2021)
- [1-Bit Compressive Sensing for Efficient Federated Learning Over the Air](https://mlsanity.com/paper/2103.16055/) (2021)
- [COKE: Communication-Censored Decentralized Kernel Learning](https://mlsanity.com/paper/2001.10133/) (2020)
- [BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers](https://mlsanity.com/paper/2110.09660/) (2021)
- [CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Data](https://mlsanity.com/paper/2208.05578/) (2022)

## Built on by (most cited first, 4 of 4)

- [Physics-Inspired Distributed Radio Map Estimation](https://mlsanity.com/paper/2502.00319/) (2025)
- [Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimization](https://mlsanity.com/paper/2412.02535/) (2024)
- [FM-RME: Foundation Model Empowered Radio Map Estimation](https://mlsanity.com/paper/2602.22231/) (2026)
- [Multi-Worker Selection based Distributed Swarm Learning for Edge IoT with Non-i.i.d. Data](https://mlsanity.com/paper/2509.18367/) (2025)

## Abstract (arXiv)

The rapid growth of Internet of Things (IoT) has led to the widespread deployment of smart IoT devices at wireless edge for collaborative machine learning tasks, ushering in a new era of edge learning. With a huge number of hardware-constrained IoT devices operating in resource-limited wireless networks, edge learning encounters substantial challenges, including communication and computation bottlenecks, device and data heterogeneity, security risks, privacy leakages, non-convex optimization, and complex wireless environments. To address these issues, this article explores a novel framework known as distributed swarm learning (DSL), which combines artificial intelligence and biological swarm intelligence in a holistic manner. By harnessing advanced signal processing and communications, DSL provides efficient solutions and robust tools for large-scale IoT at the edge of wireless networks.