Abstract

Visible light communication (VLC) networks modeled through a scheduling-level IEEE 802.15.7-oriented PHY/MAC abstraction are sensitive to line-of-sight blockage, receiver orientation, ambient-light noise, heterogeneous traffic loads, and inter-luminaire optical interference, which limits the effectiveness of fixed medium access control (MAC) policies in dense deployments. This study presents a federated deep reinforcement learning framework for distributed MAC scheduling in multi-luminaire IEEE 802.15.7-oriented VLC networks. Each luminaire is modeled as a local scheduling agent that selects the served receiver, transmission slot, optical power level, and physical-layer (PHY) mode from local queue, channel, blockage, interference, and illumination states. Instead of sharing raw observations, luminaires periodically exchange model parameters with a federated aggregation server to coordinate policy updates while preserving data locality. The proposed method is evaluated in a custom discrete-time simulator for a [Formula: see text] indoor VLC scenario with four ceiling luminaires, 8-32 receivers, stochastic traffic arrivals, receiver mobility, ambient-light noise, and line-of-sight blockage. Results averaged over 30 independent runs show that, at [Formula: see text] receivers, the proposed scheduler reduces average packet latency from 45 ms to 31 ms and 95th-percentile latency from 92 ms to 66 ms relative to the implemented resource-constrained centralized deep Q-network (DQN) baseline. Under a blockage probability of 0.3, the packet delivery ratio increases from 0.858 to 0.902, while under high ambient-light noise the packet error rate decreases from 0.064 to 0.049. The method also achieves a Jain fairness index of 0.96, reduces average synchronization overhead from [Formula: see text] to [Formula: see text] at a synchronization interval of 10 episodes, and shortens convergence time from 940 to 670 episodes at [Formula: see text] luminaires. Illumination and flicker diagnostics show that executed actions satisfy the normalized feasibility mask after filtering. These results indicate that, within the adopted IEEE 802.15.7-oriented simulation abstraction, periodic federated parameter sharing improves the scheduling trade-off by reducing delay and coordination cost while preserving mask-enforced lighting feasibility, improving empirical reliability and fairness, and showing favorable multi-luminaire scalability trends.