## 6 Challenges and Future Perspectives  
The complete DRL-based eFL or non-complete DRL/FL-assisted systems consist of inevitable challenging aspects to handle to become a practical approach in real-world deployment. The potential challenges and directions for future studies are presented in this section as follows:  
* _Malicious participant detection_: to prevent compromising participants' privacy during vertical/horizontal FL training, non-malicious participant selection is a critical phase to accurately consider in an environment of heterogeneous IoT taxonomies. Local co-training and co-sharing methods between participants for improving model performances are vulnerable to numerous possible attacks. The global learning model can be severely degraded by the false model parameters of malicious devices. A detection method using DL models (e.g., long short-term memory) can be integrated to securely execute predictive FL-enabled intelligence. * _Data contribution quality_: data contribution may differ between each participant based on sensing capabilities, hardware resources, or power levels. Data quality management and clustering can be studied to optimally select clients based on how valuably their input data influence the final model. However, it is significant to improve the low-quality data and ensure maximal participation from clients. DL models for data (e.g., images) recovery, visualization, or resolution enhancement can be a cooperative method and a part of a reliable FL framework. * _Optimal learning performance_: the convergence of the final learning model is time-consuming for reaching a satisfying accuracy in some real-time/mission-critical services, which leads to inapplicability for deployment. The upper bounds of expected convergence latencies require to be predicted or proactively known for adaptively optimizing the communication and computation resources in terms of model transmission and training rules in every communication round. A joint problem that considers the minimization of (1) system costs, (2) convergence delays, and (3) model accuracy is a prominent research direction. * _Multi-service optimization_: FL in multi-service IoT systems demands critical slicing and priority-aware orchestration schemes. The clustering mechanism outputs different labels ranging from mission-critical to non-mission-critical FL applications, which can be differed in terms of bandwidth/computational resource allocation, service prioritization, and aggregation scheduling. In the case of one particular client participating in two or more different FL-based applications, the availability and scheduling policies are interesting domains to research extensively. * _Privacy regulations_: a privacy-enhanced contribution is the major role of FL approaches; however, with numerous communication rounds of model updates/ distribution between participants, edge aggregators, and parameter servers, sensitive subsets of information can be unmasked. Differential privacy is well-known for ensuring standard individual personalization on multiparty dataset aggregation [97]. Nonetheless, there are tradeoffs between improving each critical privacy and final learning performance. A multi-criteria decision-making model or weighted sum formulation for balancing the privacy regulations and accuracy is considered a great challenge for future improvement, particularly for a multi-service IoT environment [98; 99; 100]. A DRL-based optimization approach has a high potential to evaluate the fairness of the system and provide a long-term expected assessment. * _Balancing mechanism for synchronous FL_: from the asynchronous FL perspective, there are in-depth studies for (1) enhancing the performance automation with higher efficiency, (2) advancing the privacy/security, and (3) accelerating the convergence speed within digital twin networks and blockchain-enabled approaches [101; 102; 103; 104; 105].