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Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation by Khan, Muhammad Irfan; Kontio, Elina; Khan, Suleiman A.; Jafaritadi, Mojtaba is a scholarly article available to read on EtoBox.

What is Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation about?

Federated learning (FL) enables collaborative model training across decentralized datasets while preserving data privacy. However, optimally selecting participating collaborators in dynamic FL environments remains challenging. We present RL-HSimAgg, a novel reinforcement learning (RL) and similarity-weighted aggregation (simAgg) algorithm using harmonic mean to manage outlier data points. This paper proposes applying multi-armed bandit algorithms to improve collaborator selection and model generalization. By balancing exploration-exploitation trade-offs, these RL methods can promote resource-efficient training with diverse datasets. We demonstrate the effectiveness of Epsilon-greedy (EG) and upper confidence bound (UCB) algorithms for federated brain lesion segmentation. In simulation experiments on internal and external validation sets, RL-HSimAgg with UCB collaborator outperformed the EG method across all metrics, achieving higher Dice scores for Enhancing Tumor (0.7334 vs 0.6797), Tumor Core (0.7432 vs 0.6821), and Whole Tumor (0.8252 vs 0.7931) segmentation. Therefore, for the Federated Tumor Segmentation Challenge (FeTS 2024), we consider UCB as our primary client selection ap

Author

Khan, Muhammad Irfan; Kontio, Elina; Khan, Suleiman A.; Jafaritadi, Mojtaba

Published

2024

Language

EN