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* Comparing the results for isolating the best framework.
* Deep investigation of the best framework.
A detailed dissertation of the work that led to this Technical Note is available here TBD[1].
== Choosing Federated learning frameworks ==
=== System design ===
This section describes the architectural aspects of the entire system used to test Flower and NVFlare.
==== Testing environments ====
Within the FL infrastructure, multiple parties were are involved to create a collaborative environment for training ML models. These parties, including data providers (clients) and a model aggregator (server), played play essential roles in the FL process. Data providers contributed contribute their locally-held data, while model aggregators facilitated facilitate the consolidation of the individual model updates from different parties. The interactions between these parties enabled enable the training of robust preserving models, making FL an effective approach for decentralized data scenarios.
Two testing environments were usedfor testing the frameworks: the first one is denoted as "''local"'', while the other is called "''cloud"''.
* Local environment: The local parties consist of a single desktop computer that acts both as the server and four separate clients. This configuration mimics a decentralized environment where the desktop computer takes on the roles of multiple participants, simulating the interactions and data contributions of distinct entities. As the server, it coordinates and manages the FL process, while functioning as an individual client allows it to provide diverse data contributions for training. This localized approach allowed for the use of Docker as the development environment, leveraging the power of a desktop computer assembled with an RTX 3080ti GPU to enhance performance. The power given by the NVidia GPU, allowed the use of a more complex model and the simulation of four clients on the same machine that acts also as the server. Being self-contained in a single host, the local environment is convenient for testing, especially when the focus is on the functional verification.
* Cloud environment: In this case, cloud parties consist of two embedded devices or virtual machines acting as clients, and a notebook serving as the server. This configuration facilitates a distributed learning approach, enabling the clients to process their data locally while contributing to the model’s training. The server coordinates the learning process and aggregates the updates from the clients to improve the global model. This setup ensures a decentralized and privacy-preserving approach to ML, as the data remains on the clients’ devices, and only the model updates are shared during the training process. Leveraging embedded devices as clients enables the inclusion of resource-constrained devices in the FL ecosystem, making the framework more versatile and applicable to a wide range of scenarios. The notebook acting as the server provides a centralized point of coordination and ensures smooth communication and collaboration between the clients, making the FL process efficient and effective in leveraging distributed resources for improved model performance. Of course, this environment is more complicated to set up, but it better simulates real configurations.
==== ML framework ====
==== Data Preprocessing ====
==== Model configuration ====
* unsupervised learning?
**https://arxiv.org/pdf/1805.03911.pdf
== References ==
* [1]