# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 4 Wireless Network Architectures for Energy-Efficient DFL
### DFL in Satellite Constellations Communication Networks
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Wireless Network Architectures** & **Limitations** & **Research Issues** \\ \hline Centralized Wireless Networks & Constrained resources such as bandwidth, processing time; high latency due to the need to aggregate and process data at a central server; poor performance in scenarios with poor channel conditions or when devices are located far from the central server. & Managing interference from neighboring cells; scalability issues as the number of devices increases; optimizing the constrained resource allocation; requiring highly accurate channel estimation in MIMO/mMIMO systems. & Managing interference from neighboring cells; scalability issues as the number of devices increases; optimizing the constrained resource allocation; requiring highly accurate channel estimation in MIMO/mMIMO systems. \\ \hline Distributed Wireless Networks & High backhaul energy costs; uneven energy usage and synchronization between APs during FL; pilot contamination for channel estimation. & Handling large-scale networks including a large number of devices; managing the heterogeneity of data across different devices or access points, minimizing interference when multiple devices operate simultaneously; optimizing the selection of APs to ensure efficient data routing and model aggregation. & Optimizing the UAV placement and trajectory for energy efficiency; managing UAV mobility to maintain stable communication links and ensure FL model update consistency; managing the limited computational power and battery life of UAVs, addressing the consensus and synchronization in collaboration of multiple UAVs. & Optimizing the UAV placement and trajectory for energy efficiency; managing UAV mobility to maintain stable communication links and ensure FL model update consistency; managing the limited computational power and battery life of UAVs, addressing the consensus and synchronization in collaboration of multiple UAVs. & Improving the accuracy of channel models for HAPS communication in harsh environments; optimizing HAPS placement for maximum coverage and FL task efficiency; challenges of maintaining a stable communication and synchronization link between the HAPS and ground stations or devices; & Optimizing resource allocation between devices and satellites; building robust systems to address the FL disruptions due to satellite movement and atmospheric conditions; addressing the challenges of multisatellite coordination, such as seamless handoff, interference, synchronization, and orbits. \\ \hline D2D Communication Networks & Interference management due to overlapping D2D communications, limited participation as short-range links exclude distant devices, reducing diversity; synchronization as the number of participating devices increases. & Optimization of resource allocation, computing power, and FL scheduling; selection of local agents (LAs); management of offloading ratios; CH-device scheduling; adaptation of aggregation weights and link selection matrices. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the research issues and limitations of the existing energy-efficient FL-enabled wireless network architectures.