Distributed Swarm Learning for Edge Internet of Things (2403.20188v1)
Abstract: 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.
- T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Process. Mag., vol.37, no.3, 2020.
- L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, “Federated learning for Internet of Things: Recent advances, taxonomy, and open challenges,” IEEE Commun. Surveys Tuts., vol.23, no.3, 2021.
- X. Fan, Y. Wang, Y. Huo, and Z. Tian, “CB-DSL: Communication-efficient and byzantine-robust distributed swarm learning on non-i.i.d. data,” IEEE Trans. Cogn. Commun. Netw., vol.10, no.1, 2024.
- J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. Intl. Conf. Neural Netw., vol.4, 1995.
- B. Qolomany, K. Ahmad, A. Al-Fuqaha, and J. Qadir, “Particle swarm optimized federated learning for industrial IoT and smart city services,” in Proc. IEEE Global Commun. Conf., 2020.
- S. Park, Y. Suh, and J. Lee, “FedPSO: federated learning using particle swarm optimization to reduce communication costs,” Sensors, vol.21, no.2, 2021.
- P. Xu, Y. Wang, X. Chen, and Z. Tian, “COKE: Communication-censored decentralized kernel learning,” J. Mach. Learn. Research, vol.22, 2021.
- X. Fan, Y. Wang, Y. Huo, and Z. Tian, “Joint optimization of communications and federated learning over the air,” IEEE Trans. Wireless Commun., vol.21, no.6, 2022.
- X. Fan, Y. Wang, Y. Huo, and Z. Tian, “1-bit compressive sensing for efficient federated learning over the air,” IEEE Trans. Wireless Commun., vol.22, no.3, 2023.
- M. Clerc and J. Kennedy, “The particle swarm - explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. Evol. Comput., vol.6, no.1, 2002.
- T. Tuor, S. Wang, B. J. Ko, C. Liu, and K. K. Leung, “Overcoming noisy and irrelevant data in federated learning,” in Proc. 25th Intl. Conf. Pattern Recognit., 2020.
- W. Li, J. Chen, Z. Wang, Z. Shen, C. Ma, and X. Cui, “IFL-GAN: Improved federated learning generative adversarial network with maximum mean discrepancy model aggregation,” IEEE Trans. Neural Netw. Learning Syst., vol.34, no.12, 2023.
- X. Fan, Y. Wang, Y. Huo, and Z. Tian, “BEV-SGD: Best effort voting SGD against byzantine attacks for analog aggregation based federated learning over the air,” IEEE IoT J., vol.9, no.19, 2022.
- G. Zhu, Y. Wang, and K. Huang, “Broadband analog aggregation for low-latency federated edge learning,” IEEE Trans. Wireless Commun., vol.19, no.1, 2020.
- P. Xu, Y. Wang, X. Chen, and Z. Tian, “QC-ODKLA: Quantized and communication-censored online decentralized kernel learning via linearized ADMM,” IEEE Trans. Neural Netw. Learn. Syst., Early Access, 2023.
- Yue Wang (676 papers)
- Zhi Tian (68 papers)
- FXin Fan (1 paper)
- Zhipeng Cai (42 papers)
- Cameron Nowzari (35 papers)
- Kai Zeng (47 papers)