## 6 Conclusions and Future Research  
The survey presented so far has demonstrated that the transition from supervised to FL has offered several advantages. FL preserves data privacy because it enables decentralized training across devices without sharing raw data. Moreover, FL can handle larger datasets by distributing computation across edge devices. This decentralized architecture makes FL more robust and less vulnerable to single-point failures. Exchanging model updates instead of raw data leads to lower communication overhead and reduced bandwidth requirements. Thus, the communication burden is reduced significantly, compared to centralized learning approaches, particularly in large-scale RIS-enabled networks. Another asset of FL is its dynamic adaptation to heterogeneous environments, since it can continuously update models using local data unlike supervised models that require retraining for new environments. However, FL introduces challenges that have to be addressed in order to fully leverage its potential in RIS-enabled systems. Although FL addresses privacy concerns, excessive data decentralization may lead to security vulnerability; the FL system may be susceptible to various attacks, such as model poisoning, which could compromise the integrity of RIS control mechanisms. Furthermore, convergence issues and model synchronization complexities may arise, potentially affecting the performance of RIS control algorithms. The model aggregation from devices, especially heterogeneous ones with varying computational capabilities and channel conditions, may also be a matter of concern as regards the final performance of the system. It is beyond doubt that the transition from supervised to FL has led to more efficient, secure, and intelligent 6G wireless communication systems powered by AI-controlled RISs. Still, there remains ample opportunity for innovation and advancements in the field. Hybrid learning frameworks may be created by combining supervised and FL in order to balance centralized accuracy offered by supervised learning with decentralized adaptability found in FL. Emphasis may be given on developing FL techniques that enhance model generalization across heterogeneous environments and offer rapid adaptation of RIS control algorithms to new environments or user distributions. The possibility of constructing multi-level FL architectures that leverage the hierarchical nature of RIS-enabled networks may be investigated in order to allow for more efficient model aggregation and adaptation to local conditions. Furthermore, adaptive FL strategies may be developed by designing FL algorithms that dynamically adjust learning rates, aggregation frequencies, and client selection based on network conditions and RIS configurations. In addition, integration of blockchain technology with FL and possible incorporation of blockchain cryptographic hashes into FL algorithms may pave the way for increased security and trustworthiness of distributed learning in RIS-enabled networks. Finally, FL approaches that exploit the combination of multiple bands (e.g., mmWave, THz, visible light) in RIS-enabled systems may lead to greater beamforming accuracy and enhance channel estimation. In conclusion, integrating FL with RISs offers great prospects; still, there is plenty of room for further investigation. FL-enabled RIS systems have the potential to revolutionize wireless network performance and user experience. Future research should focus on developing innovative FL algorithms tailored to the unique characteristics of RIS-enabled networks, paving the way for more efficient, secure, and intelligent 6G systems. Conceptualization, E.A.Z., G.S.L. and I.O.V. ; methodology, E.A.Z., G.S.L., A.T.B., C.D.N., M.P.I. and I.O.V. ; software, E.A.Z., A.T.B. and C.D.N. ; validation, E.A.Z., G.S.L., A.T.B., C.D.N., M.P.I. and I.O.V. ; formal analysis, E.A.Z., G.S.L., M.P.I. and I.O.V. ; investigation, E.A.Z., G.S.L., M.P.I. and I.O.V. ; resources, E.A.Z. and I.O.V. ; data curation, E.A.Z., G.S.L., A.T.B., C.D.N., M.P.I. and I.O.V.