The multi-objective optimization problem of maximising the accuracy of the global model, minimising the communication cost, minimising the variance of the accuracy, and minimising the privacy budget is solved by NSGA-III. The experimental results show that the algorithm proposed can effectively reduce ...
Serving as a pivotal technology within the realm of privacy-preserving computation, federated learning employs a mechanism wherein a central server trains a shared global model while keeping sensitive data stored locally within each participating institution, and thus ensure the preservation of privacy ...
much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and data-driven multi-objective
scanner types, data modalities, and patient outcomes24,25. With the growth of medical data volumes, integrative analysis of multi-center data is becoming increasingly important to accelerate clinical workflow quality and quantitation. Federated learning (FL)26,27,28methods learn a base...
Google’s [30], [31] research team proposes federated model averaging algorithm for the model aggregation. It uses a deep learning based on Tensor Flow and supports differential privacy method to enhance privacy guarantees to achieve a local model with minimal communication rounds. Recent research ...
These variables also define the number of communications that the algorithm will do at the Edge and towards the Cloud. In particular, the algorithm first sends the initial model from the CA to the EANs and then from the EANs to the ECNs. Once the ECNs have the initial model, they start...
(5)). The learning process is summarized in Supplementary Algorithm1. We trained 200 epochs for all tasks and updated each discriminator once in each training iteration. The gradient-based updates can adopt different gradient-based learning rules. We used Adam optimizer85with a learning rate of ...
Furthermore, most existing work on FL measures global-model accuracy, but in many cases, such as user content-recommendation, improving individual User model Accuracy (UA) is the real objective. To address these issues, we propose a Multi-Task FL (MTFL) algorithm that introduces non-federated ...
Matching to Optimise HOTALike in MOTA (and IDF1) the matching occurs in HOTA to maximise the final HOTA score. The Hungarian algorithm is run to select the set of matches, such that as a first objective the number of TPs is maximised, as a secondary objective the mean of the association...
In contrast, the (CNN) model incurred the shortest training time, averaging 2.3 minutes. It’s worth mentioning that the (CNN) model, while computationally expensive and resource-intensive due to its large number of trainable parameters (6,505,349), benefits from early stopping, requiring the ...