## 2 Related Work  
### Dynamic Federated Learning  
Federated learning (FL) is particularly useful in dynamic environments where data and models evolve over time. However, this scenario presents unique challenges and opportunities. For instance, Models must adapt quickly to new data and changes in user behavior, we need to track down these changes and noises. Also, data quality and device availability are quite difficult to foresee; therefore, the system must address these details. As described by Research Questions in Section 1, privacy must be preserved even in a fast-moving environment. Aside from that, federated learning comes with different opportunities and is the key enabler technology for the following: Models can be continuously updated with new data, improving their performance over time and in real time. Also, distributing the computing load improves the overall efficiency [3]. To wrap up: FL in dynamic environments is an evolving research area with the potential to transform AI in industries such as healthcare, communications, finance, and the Internet of Things (IoT). Projects such as [4] are similar to our proposal but do not tackle the complexity of having heterogeneous environments such as different architectures or models. For instance, [PERSON] et al. in [5] employ a system that is built using high-performance commercial hardware such as Nvidia Jetson. Technologies enabled by this approach are also analyzedby [PERSON] et al. in [6]; however, the work is focused more on the enabled scaling offered by Kubernetes than on the advantages or heterogeneity of the devices or models. The allocation of resources is covered in [7], in this work by [PERSON] et al., an in-depth analysis of the allocation of resources is presented; we focus on a slightly relaxed problem, avoiding the allocation of tasks but focusing on the performance of heterogeneous device development without complex scheduling between them. The combination of these analyses could be valid in future work. Another interesting project is presented in [8] by [PERSON] et al., which focuses on the employment of an aggregator that runs on Kubernetes, focusing on resource distribution and aggregation. We explored the possibility of having a simpler distribution method without evaluating the complex algorithms that can move the results of our analysis from the heterogeneity of the devices. Privacy is an explored field in the federated learning analysis, for instance, projects such as [9] include modern technologies such as homomorphic encryption in the field. While being complex in terms of implementation and management, we focus on simpler behaviors, based on the data themselves. Homomorphic encryption is also a computation-intensive task, making it difficult to implement on mobile or IoT devices. Differential privacy is explored in projects such as [10,11] and collected in surveys such as [12], which covers techniques used in 2022. From these works and their promising results, we took inspiration to introduce differential privacy into federated learning and analyze it in heterogeneous architecture environments. Federated learning also covers different infrastructures with different setups such as serverless infrastructures [13]. While these infrastructures are focused on the maximization of resource usage, it is not easy to port them to different architectures or to configure them to perform in heterogeneous environments. On the other hand, technology enablers are analyzed in depth in the field, for instance, works such as [14] cover the analysis of Kubernetes applied to blockchain-based federated learning. We do not want to tie our system to a specific technology such as Blockchain or DHT, making it simpler and focusing the effort of our work on an analysis of the behavior of heterogeneous architectures in Federated Learning scenarios. This is also the case of vertical applications such as 5G networks and beyond such as 6G networks [15,16].