<p>Wireless Federated Learning (FL) has emerged as a promising paradigm for distributed intelligence in bandwidth- and energy-constrained environments. However, excessive communication overhead, network heterogeneity, and unreliable wireless links remain major barriers to practical deployment. This paper presents a PRISMA-guided systematic review of communication-efficient protocol designs for wireless FL. Thirty peer-reviewed studies published between 2020 and 2025 are analyzed and organized into a structured six-category taxonomy comprising compression and quantization; client scheduling and topology control; aggregation and security; transmission design; privacy-preserving mechanisms; and model update strategies. To enable systematic comparison, the reviewed protocols are evaluated using six wireless-centric performance metrics: communication overhead, model accuracy, latency, energy efficiency, scalability, and implementation complexity. Quantitative benchmark summaries, comparative radar visualizations, and privacy–communication trade-off analysis are employed to highlight strengths and limitations across protocol categories. In addition, communication-efficient strategies are mapped to representative deployment scenarios, including UAV swarms, industrial IoT systems, vehicular networks, and edge-enabled 5G/6G infrastructures. The findings demonstrate that no single protocol family universally optimizes all performance dimensions; instead, effective wireless FL requires deployment-aware, cross-layer co-design that balances efficiency, scalability, latency, and security. By integrating methodological rigor with system-level insight, this review provides actionable guidance for developing robust, communication-efficient, and privacy-aware wireless FL systems in next-generation edge-intelligent networks.</p>