Editing Federated Learning (section)

== 5.6.3 Adaptive Client Selection Methods ==

Adaptive client selection methods focus on optimizing the selection of participating clients during each training round to enhance resource utilization and overall model performance. Approaches include:

* '''Resource-Aware Selection''': Prioritizing clients with higher computational capabilities and better network connectivity can lead to more efficient training processes. By assessing the resource availability of clients, the FL system can make informed decisions on which clients to involve in each round.<sup>[4]</sup>

* '''Clustered Federated Learning''': Grouping clients based on similarities in data distribution or system characteristics allows for more efficient training. Clients within the same cluster can collaboratively train a sub-model, which is then aggregated to form the global model, reducing the overall communication and computation burden.<sup>[5]</sup>

* '''Early Stopping Strategies''': Implementing mechanisms to terminate training early when certain criteria are met can conserve resources. For example, if a client's local model reaches a predefined accuracy threshold, it can stop training and send the update to the server, thereby saving computational resources.<sup>[6]</sup>

Incorporating these strategies into the FL framework enables more efficient utilization of the limited resources available on edge devices. By tailoring model training processes to the specific constraints of these devices, it is possible to achieve effective and scalable FL deployments in edge computing environments.