mlsanityData to 13 Oct 2026

Distributed Swarm Learning for Edge Internet of Things

arXiv 2403.20188 ↗First posted 29 Mar 2024cs.NI cs.AI cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 29 Mar 2024.

Builds on

9 of 17 references.

References · 9

  1. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  2. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 190
  3. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
  4. 2020Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Servicescited by 18
  5. 2021Joint Optimization of Communications and Federated Learning Over the Aircited by 14
  6. 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Aircited by 11
  7. 2020COKE: Communication-Censored Decentralized Kernel Learningcited by 8
  8. 2021BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackerscited by 6
  9. 2022CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Datacited by 4

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025Physics-Inspired Distributed Radio Map Estimationcited by 2
  2. 2024Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimizationcited by 1
  3. 2026FM-RME: Foundation Model Empowered Radio Map Estimationcited by 1
  4. 2025Multi-Worker Selection based Distributed Swarm Learning for Edge IoT with Non-i.i.d. Datacited by 0