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Asynchronous federated reinforcement learning for scalable load balancing in software-defined networks
Jha, A.K. Mahendran, A. Ghafir, Ibrahim Hamada, M.
Jha, A.K.
Mahendran, A.
Ghafir, Ibrahim
Hamada, M.
Publication Date
2026-06-06
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© 2026 The Author(s). This is an Open Access article distributed under the Creative Commons CC-BY license (https://creativecommons.org/licenses/by/4.0/)
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2026-04-23
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jha_et_al_2026.pdf
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Abstract
Software-Defined Networks face increasing load-balancing demands under heterogeneous and time-varying traffic. This work proposes an asynchronous federated reinforcement learning framework with a hierarchical controller topology and selective parameter sharing tailored for SDN. Using PPO with asynchronous aggregation, the approach reduces end-to-end latency and communication overhead versus centralized and synchronous baselines, and maintains high throughput across most traffic regimes. Performance under medium and very high traffic shows degradation in load-imbalance for certain settings, indicating robustness limitations that motivate adaptive staleness bounds, traffic-aware participation, and reward reweighting. The hierarchy supports regional-to-local coordination and preserves data locality for multi-domain deployments.
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Published version
Citation
Jha AK, Mahendran A, Ghafir I et al (2026) Asynchronous federated reinforcement learning for scalable load balancing in software-defined networks. Discover Computing. (29): 320.
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Article